How Artificial Intelligence is accelerating the Energy Transition · 2019/12/2 · 2 Sales and service: growth hacking, base management and next-gen customer service 3 Solutions

1

How Artificial Intelligence is

accelerating the Energy Transition

An overview of AI activities at E.ON

Juan Bernabé Moreno Matthew Timms

Karsten Wildberger

2 3An overview of AI activities at E.ON

Preface ......................................................................................................... 4

Introduction ................................................................................................ 12

Chapter 1 – data and AI at E.ON – Data.ON ............................................... 14

Chapter 2 – responsible AI ......................................................................... 19

Chapter 3 – the Data.ON transformation programme ............................... 24

Data Readiness (DARE) ..................................................................................... 27

Data evangelisation ........................................................................................... 32

Data.ON Hubs: a global-local model of engagement ..................................... 34

Architecture, Best practices and Services ....................................................... 35

AI Readiness ....................................................................................................... 37

Data Incubation Lab ........................................................................................... 38

Chapter 4 – our delivery activities ............................................................. 46

Summary of AI powered Energy Solutions ..................................................... 47

Chapter 5 – intelligent networks ............................................................... 58

Power Networks and Smart Agents ................................................................ 59

Chapter 6 – Energy Economics .................................................................. 68

Chapter 7 – renewables and smart Assets ................................................ 72

Chapter 8 – Growth Hacking & Customer-related Analytics .................... 78

Chapter 9 – outlook ................................................................................. 100

About the authors .................................................................................... 104

Contents

How Artificial Intelligence is accelerating the Energy Transition

Juan Bernabé Moreno (Chief Data Officer, E.ON SE)

Matthew Timms (Chief Digital and Technology Officer, E.ON SE)

Karsten Wildberger (Chief Operating Officer Commercial and Board Member, E.ON SE)

in collaboration with the E.ON SE Data.ON team

Munich, May 2019, all rights reserved. Do not distribute.


Dear reader, Our AI team has asked me to share a few thoughts on Artificial Intelligence (AI) and provide a preface for this booklet. I am delighted to do so. Ever since we started our AI journey, in early 2017, we have been showing encouraging progress with many excellent contributions from our Advanced Analytics, Data Lab and AI teams. I’m proud of what has been achieved in great collaboration with the business.

What is AI and what does it mean for business? The term Artificial Intelligence (AI) refers to the theory and development of computer systems able to solve problems or perform tasks normally requiring human intelligence. There are numerous problems and tasks that require different degrees of human cognitive capabilities. In a simple model, one can distinguish three levels of intelligence.The first level comprises seeing, observing and making predictions from observations. This is like analysing data to solve a special problem or task. The second level is about doing, intervening and decision-making – being aware of the consequences of one‘s actions. On this level, solving a problem may enhance the capacity to solve other problems even in different fields using analogies and transferring knowledge. And the third level – the highest level of thinking – is about understanding, imagination and counterfactual reasoning. This is the highest level of abstraction where abstract thoughts and “fantasies” become reality in themind.

Most of today’s AI solutions operate on the first level, learning from data using refined mathematical models to make predictions. Machines have become

much better than humans in many fields of (specialised) activities using powerful prediction algorithms, e.g. playing chess or playing the abstract strategy board game Go, recognising pictures and patterns, translating texts into different languages, recognising speech, driving autonomously, etc. So, we are in the prediction era. Powerful prediction tools can profoundly enhance human decision-making.

What does AI mean for business and industries?

The impact of major innovations is often felt in unexpected places. Important and impactful innovations usually cause a relevant fall in price. The Internet, for example, reduced the price of distribution, communication and search.Reframing a technological advance as a shift from expensive to cheap or from scarce to abundant is an important way of thinking of how new technology may affect businesses. Current AI tools lead to more accurate predictions and better decision-making. Hence, the cost of predictions will be cheaper. This will increase efficiency, reduce costs and improve the customer experience. Based on AI algorithms, we are able to reduce maintenance spending in our energy networks by making more accurate decisions, such as which equipment needs maintenance or replacement. Or we can lower our cost of acquiring customers by identifying customer segments interested in our products and services.

In the long run, AI could lead to the next level of industrial automation. It could replace the type of automation we have known to date which is based on standardisation and economies of scale. Future AI-driven automation may not need the standardisation and structures we are familiar with today. But this is a more long-term scenario.

Currently there is a lot of hype around AI. Much of the debate is fuelled by fear and anxiety, and the question of when machines and algorithms will reach the level of human intelligence and take over the world. We’re still very far away from such a scenario. The point at which machines surpass human intelligence isn‘t around the corner. Clearly, also today there are very important ethical questions to be debated and answered because AI will impact all aspects of our lives. It will probably do so in a more profound way than any other human innovation. AI will thoroughly impact our jobs, and in particular, how we’ll live our lives. It poses fundamental ethical questions regarding humanity. But let’s not forget that morality and ethics aren’t questions of machines but of humans.

In order to harness the opportunities and manage the risks of AI, one needs to actively embrace and engage with the technology. Only if you know how

Preface

Karsten WildbergerMember of the Board of Management of E.ON SE

Preface


For example, the last two years have been particularly successful for E.ON’s Data.ON programme and our AI activities overall. Our AI community has taken our capabilities to the next level and successfully developed important solutions in close collaboration with the business.

For E.ON, digitisation and AI means considering how we can combine the physical asset world with the digital world. We aren’t a digital pure player – so it’s really about the synthesis of these two worlds. That said, we should remain curious as to whether there might be business models in the future energy world that exhibit the characteristics of digital pure players.

something works, you can control it. However, the foundation of our work has to always be based on a clear set of values guiding us.

What are my AI highlights so far?

We’ve come a long way in Advanced Analytics and AI in the last two years. We’ve built a high-calibre team that makes a huge difference to E.ON. We are taking the right approach. We engage with the business in multiple promising “AI projects” prioritised based on value and impact. We are learning fast: adjusting, stopping or growing depending on progress and what we’ve discovered. There is no “AI Big Bang”. Clearly, we need to create and show the value of our initiatives and increase impact over time.

Here are my personal highlights across our five major areas of activity:

1 Smart assets and networks: predictive maintenance, local balancing and network monitoring 2 Sales and service: growth hacking, base management and next-gen customer service 3 Solutions Analytics: disaggregation, PV Analytics, Optimum and Future Energy Home 4 Intelligent Energy Economics: network zone balancing, short-term yield prediction 5 Renewables (now with RWE as part of the Innogy

acquisition): dynamic yield optimization

Preface

You will find exciting results of E.ON’s achievements in this white paper.

I feel proud of what has been achieved and how the teams have approached the work – looking for impact and creating solutions with the business.

What are the critical factors for continued success?

There are multiple success factors. But let me single out one: attitude. Attitude is the basis for everything. If the right attitude is missing nothing can compensate for it.I put attitude ahead of skills or investment. With the right attitude one can achieve almost anything. One shouldn’t underestimate the human capacity to learn new things. Investment without the right attitude is unlikely to pay off. And with the right attitude we’ll also make the necessary choices to allocate sufficient resources.

So, if we want to become an energy company that leads in a digital and AI world, we need to create a data-driven culture and be curious about new ways to do business. We need the courage to focus, stop things when they aren’t working and grow and scale solutions to their full potential when they are promising.

What are the priorities in AI going forward?

I see three areas where we should reach the next level. 1 – data culture• How can we become a data-driven business harnessing the power of data? • How can we considerably improve data quality and make data accessible

and available?• How does data quality and availability become a key consideration of everything we do? Equally as important, we need to deal with data in the right way: we must never compromise on privacy or data security. Protecting customers’ data and privacy is of utmost importance. And we need to always follow the law and beguided by our values. However, within these parameters, so much more can be done. We just have to do it the right way.

2 – scale and impact• How can we scale our solutions faster and reach greater business impact? • How can we grow solutions to their full potential?• How can we step up in our leadership to move from piloting to true business

change?


• How can we measure the impact in financial terms and quantify commercial benefits? This can’t be done by the AI teams themselves. This requires buy-in from the business and leadership on all levels.

3 – industry-changing business modelsMany AI applications improve efficiency and optimise our business based on better predictions and decision-making.

• Where can AI be less “predictive” but more disruptive and with greater impact?

• Where are the areas where E.ON can gain a relevant competitive advantage over competitors?• Which new business models may emerge that have the potential to create relevant new value pools?• Does this have to be within our current understanding of the energy world or

can it sit outside? • Do we need to broaden our perspective? We live in times in which innovation is influencing our lives more profoundly than ever before and in which the rate of innovation is accelerating. The digital and increasingly AI-based economy is a reality.

We live in a world where powerful platforms emerge with billions of customers connected, a world in which everything gets connected to everything. We live in a world where data is becoming the powerful source of insights. Processing data and generating insights will, in turn, generate more data and deeper insights.

Therefore, not embracing AI is not an option. Not participating or participating half-heartedly means stagnation, going backwards and eventually going out of business.

Only companies that actively push for innovation, and thus AI, will be the ones that can be successful in the future. A prerequisite is a curious and positive mindset guarded by a strong set of values always reminding us of what’s right and what’s wrong.

We are already writing the next chapter of AI-based innovation and it is really exciting!

Best wishes, Karsten Wildberger Member of the Board of Management of E.ON SE

Dear reader, Hardly any other technology currently receives such attention as technologies summarized under the term „artificial intelligence“ (AI). And not without good reason, because the use of AI applications will change many areas in a similar way as computers or the internet - and the energy industry is no exception.

AI provides significant potentials for the energy industry and the success of the energy transition

The energy world is changing rapidly: nuclear and coal phase out, expansion of renewable energies, the associated volatility, flexibilisation and decentralisation, ramp up of electromobility, decarbonisation of gas and district heating as well as the ever stronger interlinking of previously largely separate economic sectors (sector coupling) - in short, many things are changing and becoming more complex.

AI applications will support the control and optimisation of these increasingly complex energy systems and help to control them more effectively. The possible applications of artificial intelligence in the energy industry are manifold and often not only have a positive influence on the success of the energy transition, but also make sense from a business point of view. The examples given in this book demonstrate this vividly.

Challenge: Reduce misconceptions and make AI accessible

Looking at the current state of the technology it is clear that there have been exponential developments in recent years, especially in the field of machine learning. As a result, AI technologies are already able to perform many tasks much better than humans or conventionally programmed software could. The examples of speech or image recognition in smartphones or digital assistants show that this development has already found its way into our everyday lives almost as a matter of course. The potential of this rapid development must also be utilised in the energy industry.

1 See e.g. BCG Gamma (2018) – Artificial Intelligence: Have no Fear. Available at: https://www.slideshare.net/TheBos-tonConsultingGroup/artifici-al-intelligence-have-no-fear

Preface

Kerstin AndreaeChair of the BDEW


Special thanks to the book contributors

Adam HitchmanAlberto DoriaAlexander SchaafAlexander SchulzAttila TozserChristine HofmannCorey O‘MearaDenis FanDina YounisElena OserErik Sebastian JäckelErika DegouteFelix VölkerFlore de DurfortGerrit WiezoreckGiorgio CortianaHeiko SelingerIman KamehkhoshInko Elgezua FernandezIrfan MaroofJan KochJens GräfJuliane KruckKateryna ZabolotnaKaweh Djafari NainiMayur Sand

Louise EgremontMartin ClevenMeiwen JiaMohammad SaifullahNarcisa Maria PantilimonNarisu TaoPhuong TranQuentin CangelosiRia van HeckeRobert EigenmannRoland RoddeRomina MediciSam JulianSebastian SchwarzSergiu ChelaruSimon SingerStefan BirrThomas SpuraTim Harbord

… all our business units supporting us and believing in the power of data and AI

… the entire data science community at E.ON

One problem is that in the public perception artificial intelligence is often linked with negative associations (e.g. surveillance or job losses) or with exaggerated expectations (general artificial intelligence). In an international comparison, concerns about the technology dominate especially in Germany.1

Often there is no realistic understanding of exactly what artificial intelligence is and how it can be applied today with real benefits. The potentials are assessed more positively and realistically wherever a concrete application of AI could be experienced. This book makes a good contribution to this, because it shows how artificial intelligence can be used in different areas of the energy industry. It also shows how companies can deal transparently and responsibly with ethical issues associated with the use of new technologies.

BDEW project on AI in the energy industry

The BDEW as the central association of the German energy and water industry actively supports its member companies in exploiting the opportunities offered by digital transformation.

For this reason, we launched the „Artificial Intelligence for the Energy Industry“ project together with our member companies at the beginning of 2019. The aim here is to classify the technology for the energy industry in an understandable way, to collect use cases and to give practical tips for the deployment of artificial intelligence.

This book with the case study AI at E.ON should therefore be the first publication in a series. The diversity and maturity of the use cases presented by E.ON show that AI is not just a topic for the future, but is already being used successfully in the energy industry today, e.g. to improve efficiency, better serve the customers, reduce CO2 emissions and change the way companies work in general. AI is therefore not only a reality today at E.ON and other energy companies, but also a key driver for the transformation of the energy world.

Our goal as BDEW is to provide ideas through practical examples and recommendations for practical implementation, to allow a focused introduction to the applied use of AI.

Only if we succeed in making artificial intelligence accessible to all actors in the energy industry can the full potential of technology be unlocked.

Yours,

Kerstin AndreaeChair of the BDEW


Artificial Intelligence allows us to approach our work, our environment and our lives from a completely new and enriching point of view. This set of technologies can revolutionise the way we do things, how we interact with our environment and with each other, how we create and consume products and services, how we treat diseases or how we address the climate change. AI has the potential of changing the majority of the facets of our lives. Apart from the well-known hype, the disruptive potential is indisputable. AI also introduces new challenges for which we do not have a definitive answer yet, such as the ethical dimension of the AI algorithms or the moral code of robots. At the same time, AI opens a new world of opportunities, which we need to learn how to realise. But if we succeed, AI will definitively make us take a qualitative leap in the progress of humankind.

AI is not something that is reserved for a near future. AI is changing our society as we speak, and reshaping our energy world as well. In order to understand why we are talking about AI now, we just need to look back a couple of years. We all probably remember a time where everybody was fascinated about big data. Big data was seen as the next revolution, the thing that was going to transform the way we live. Under this premise, companies have been gathering massive volumes of all kinds of data and investing in new technologies to work with this data for the last 5 years. This trend is not predicted to slow down, on the contrary, as IoT matures and sensor technology becomes more affordable, the volumes of data we generate and store will continue to increase exponentially. By 2020, there will be 26 billion devices connected to the Internet, equivalent to 63 million connections per second.1

Introduction

1 https://www.gartner.com/en/documents/3882671

Fig 1: Google trend global searches for „Big Data“, „Artificial Intelligence“ and „Machine learning“

Given the volume and variety of data nowadays, we firmly believe that AI has the power to answer fundamental questions and solve fundamental problems that are preventing us from moving forward with the energy transition. Hence, we are comfortable saying that AI is reshaping the energy world as we know it.

In this book, we would like to provide tangible examples we have implemented at E.ON in the course of the last 2 years. Apart from creating a huge pull from the organisation and delivering tangible value in all strategic areas across E.ON, we kicked off our transformation programme by mid-2018 and we managed to harvest sound success. Ever since we started 2 years ago, data and AI have achieved a quantum leap at E.ON, and the best is yet to come as we real the benefits.

After years of data gathering, the industry started asking the question “But what happened to the value promised? How can we extract the value outof this data?” We have come to a point where everybody is looking for ways of harnessing the power everybody claimed was in the data. Machine learning initially, and the broader and more commercial term “Artificial Intelligence” (AI) a bit later, have been on the rise for the last 2 years and have even overtaken big data as we can see in Fig. 1. Everything is about AI, everybody is talking about AI and if you are not involved in AI, you are most probably suffering from terrible FOMO (fear of missing out). But the search for value triggered a new hype.

At the moment, the term AI is so overhyped that people who have been working in the field for years even feel a bit uncomfortable bringing it up. The issue starts with the definition, which easily takes worrying turns (humanoid technologies, humankind-threatening machines that will annihilate the human race).

Hence, I will keep it very simple. For me and for most of the practitioners:

“AI is a set of tools that allows us to find the right answers to questions we didn’t know how to tackle before, based on data.”


Chapter 1 – data and AI at E.ON

Data.ON

Back in 2017, the Digital.ON programme was born with a clear goal of implementing high-impact projects – Our „so called Big Tickets“ –, to advance the digitalisation of the company and harness substantial value from various strategic pools. Most of the Big Tickets have relied on data and AI components delivered out of the central Advanced Analytics team. At the beginning of 2018, Data.ON (our Data and AI programme) was kicked off with a more specific focus on data-related delivery and transformational activities.

It only works with the right skills and the right driveThe Data.ON central team counts with more than 50 data professionals, being 10 when the unit began in Q2 2017. In parallel to the central team, almost each and every regional and business unit has onboarded data professionals in various functions (energy economics, retail, solutions, etc.), growing the community of data practitioners across E.ON. The complexity of creating AI systems is not negligible and requires employees with highly specialised profiles in several disciplines (advanced statistics, mathematics, machine learning, software development, deep domain knowledge, visualisation, data storytelling and communication skills, etc.). Data professionals are in high demand and attracting the right talent is very challenging.

“The ability to turn data into value is an opportunity we must bring to reality in order to make a difference. Behind each and every disruption, there is always a supporting data component. Data is already reshaping the whole energy world, which is moving us towards a carbon-free society. The importance of data and the tools to turn data into value (aka Machine Learning or Artificial Intelligence) is indisputable. Everybody is talking about ‘doing’ AI, every company is heavily investing in AI, just like E.ON. Talking is easy, but we prefer to ‘do’ and let the results ‘talk’ for us.”

Data.ON rubric

Fig. 1 development of internal headcount in the central Data.ON team


What makes E.ON attractive for data professionals?

The possibility to make a difference that matters in the grand scheme of things. At E.ON, data skills can be directly applied to drive something as essential as the energy transition. What we do actually matters beyond company economic results, and this is only possible in a few selected companies and industries.

Our triple role: deliver, transform and innovate

E.ON’s expectations of the Data.ON team are threefold. First and foremost, it is about delivering measurable value to the organisation. AI is the perfect tool to harness the value of our data sources and it is critical to prove it by delivering high-impact, high-visibility projects all across the company. Our delivery activities are grouped by strategic area to set the right focus and pursue continuous engagement with the same stakeholders and develop domain knowledge in the team. These areas are energy networks, energy economics, renewables - at present less focus on large-scale, after the Innogy deal with RWE - and smart assets, eMobility, digital customer solutions and marketing, sales and services.

Secondly, we are data transformation agents, which means we are responsible for driving ”datarisation“ - a term coined by internally referring to the ability to exploit the data capabilities of a company. We are implementing it in our Data.ON Transformation programme, which will be described later in this document.

Thirdly, we are increasingly being challenged to create new business models based on the data, in other words, to pursue a bottom-up innovation approach with a clear disruptive focus and the expectation of landing tangible results.

Diversity as a source of strength

We count 17 nationalities from different backgrounds (mathematics, physics, computer science, chemical engineering, economics, etc.). Scientific work yielding the best applied resultsWe put a lot of emphasis on the “science” part of data science, having a remarkable share of colleagues with PhD qualifications with countless publications and patents, exploiting the best sides of research and hands-on delivery.

Attractive locationsApart from Essen, Munich is a very attractive hub for data professionals with a very mature data science scene – with meet-ups, hackathons, universities and start-ups.

Chapter 1 – data and AI at E.ON – data.ON

Needless to say, all three disciplines are heavily interconnected, as transformation creates the foundations for a better and faster delivery of all the innovation activities, providing the right ecosystem of partners. On the other hand, the delivery of our projects benefits hugely from the exploration work performed in our innovation track and thus, many of the ideas and assets we use to run data-driven innovative prototypes stem from the projects we are delivering.

Tremendous appetite and strong managementsupport The fact that AI is being hyped is very beneficial to our team and puts, at the same time, some healthy pressure on the organisation to show progress in this field. This is probably happening in all mid-size to large companies in the world, but the majority of them don’t manage to cross “the AI chasm” and stay with visually appealing proof of concepts that might work in theory, but fail to create a considerable impact.

The situation at E.ON is quite different, mainly because we count on the strong support of the management board, especially Dr Karsten Wildberger, but we are also given clear values and goals to deliver against. To make it concrete, each initiative we engage in needs to provide hard KPIs and is subject to continuous review based on these KPIs. That’s a key success factor introduced by our top management for driving the digital transformation, which forces us to stay focused and outcome-orientated.

Our main challenges

E.ON is a large and complex organisation with many structures and processes, legacy IT systems layering up complexity over several generations, and quite new in the quest to harness the value of our data. Almost all the challenges we provide below are part of our day-to-day work when we deliver data projects. Our Data.ON Transformation programme, as we will see in the next section, is tailored to address these specific challenges: • Awareness of the power of data: while we gather a lot of data from our networks, eMobility infra-structure, smart meters, etc, we still need to make everyone see that data can really boost our performance and create new opportunities

• Data literacy and skills: like many other non-tech companies, E.ON is investing in increasing data literacy and deploying the right AI skills, but it is still a long journey given the size of the company and the number of strategic areas.


• Resistance to change and sometimes, fear of AI: lack of understanding makes some people develop a hostile attitude towards AI, as it is wrongly perceived as a job-stealing threat, that‘s why we are putting such a strong focus on increasing the data literacy

• Fanciness and fragmentation: the downside of the hype manifests itself when several initiatives get started in different parts of the organisation in an uncoordinated way, sometimes trying to solve the same problem that has been solved already in a different department, usually not reaching a sufficient maturity level to be exploited in a value-generating context (i.e. just for show). This issue is particularly important, as otherwise, if the

value remains unharnessed, the perception is that AI failed and our overall credibility is undermined.

• Data protection is non negotiable, but GDPR is to often used as an excuse. More education on the relationship between AI and data protection is needed. This would ensure the business is more aware of the areas where GDPR does not apply.

• Uncoordinated engagement with vendors, lack of real partners: we had to deal with many initiatives triggered by our own vendors trying to position their services or selling their proprietary software.

Chapter 2 – Responsible AI


As the adoption of data-related technologies increases, companies such as E.ON face new challenges. A future-oriented, privacy-protecting data science practice comes down to how data professionals behave.

Due to the complexity and variety of possibilities of AI, we welcome the discussion on ethical data but algorithms should not be placed under general suspicion that require strict regulation and bureaucratic processes. What´s needed in terms of digitalization is a clear and smart regulation, education and the willingness to invest.

The morality of a knife

If we see AI as just a tool or a set of tools to “squeeze” value out of data, the morality question moves irremediably to those using the “tool”. Depending on who is in control of the tool, the value extracted might be a benefit or a curse for our society. In the same way a knife, which is also a tool, can be used for multiple beneficial purposes, e.g. hunting, or cutting materials, at the dining table, etc. a knife in the wrong hands can take lives, is a knife a curse or a cure for our society?

The knife example is a bit too simplistic, isn’t it?

Definitely, because unlike AI, everybody understands how a knife works, the result of using it is deterministic (will cut/hurt) and everybody can label the actions of using it as right or wrong. AI is obviously more complex, but the complexity drivers come from the pure definition of AI, a set of techniques to turn data into value: • AI learns from the data and from the patterns in the data. If, for example, a model is trained with many attributes to allow the

answering of the question “what is the likelihood of a suspect to have actually committed a crime?” and the model has been given behavioural patterns as attributes, such as the distance to the crime scene, number of people connecting the suspect and victim over social media, etc., probably nobody bats an eyelid, but what if we include in the training elements some more personal attributes, such as gender, race and nationality? Probably we will get a racist and overly minority discriminating system. Does that make AI racist?

• There are serious limitations going beyond the learned data.

For example, take a self-driving car trained on the streets of a big city to the country roads of Bavaria, then most likely, the system won’t know how to react when a wild pig crosses the road on a dark winter’s night. The reason is simple, the system has never seen a wild pig before and has never learned how to anticipate their behaviour.

• AI is difficult to understand. AI is basically made of data and mathematically inspired routines. The

degree of complexity is high; thus, many people consider AI as a black box: “why/how did your AI algorithm come up with these answers based on these data?” We tend to fear what we don’t easily understand. There’s a whole research field devoted to making AI more understandable for everybody, called “explainable AI”, as it has been identified as a major acceptance problem. We are experimenting with explainable AI (LIME or local interpretable model agnostic explanations). For example, our churn for residential customers in Sweden can point to the input factors that determine a customer‘s likelihood to churn.

• AI is really good at problem solving when the problem is well defined and sufficient data is available. It outperforms human beings, for example, in figuring out a diagnosis of an

x-ray picture, and we struggle to accept that anybody could train a machine that could diagnose particular cases better than a highly specialised doctor with 20 years of experience.

• If not done properly, even well-intentioned AI can lead to unpredictable consequences. Professional data science skills are required to make sure the proper learning algorithms are employed on top of the proper data sources and the outputs are used in the proper way. Drag & drop approaches to AI are becoming popular, but they don’t replace the expertise required to responsibly implement AI.

What about creating intelligent machines capableof replacing us?

Human intelligence is still and will for a long time be out of the reach of machines. We can create imitation machines, capable of mimicking human capabilities astonishingly well, such as vision (object recognition1), speech (the WaveNet project- which with sufficient training can mimic anybody‘s voice with such precision that people on the phone couldn‘t tell the difference), writing interactions (chatbots) and even facial expressions3.

1 Framework YOLO (You Only Look Once v3), available at https://pjreddie.com/ darknet/yolo/

2 Google WaveNet Project, available at https://deepmind.com/blog/wavenet-generative-model-raw-audio

3 Deep learning techniques to learn facial emotions, athttps://medium.com/@HP_Analytics/deep- learning-forfacial-analysis- 91cafc9c8985

Chapter 2 – responsible AI


Are these machines really “intelligent” or “human”? No, they just exhibit successful implementations of imitating technologies. What’s missing? Everything else! Context, imagination, dreams, creativity, emotions, etc. Superhuman intelligence is a myth. Kevin Kelly explains it very well in his article “The myth of a super human AI”.4

The rather philosophical debate around technical singularity, pointing to an intelligence explosion where human beings are going to be surpassed and no longer in a position of defining what machines can and can’t do, is quite far away from us. Without closing the door to it, I don’t think anybody reading this piece of work now will witness it. All the debate on AI taking away our jobs is nothing different to any technology evolution in the history. Rather, AI is going to “augment” not “replace” us. It will augment doctors saving lives, it will augment firefighting, through early warning systems or augment the quality of life of our elders. AI will therefore not take our jobs, but amplify our skills to create a new set of jobs. But let’s acknowledge that AI will also augment criminals, fundamentalism groups, politically manipulative forces and other digital threats. AI cannot be locked in a box for the exclusive use of a privileged few. AI has been democratised and maybe the only entry barrier is the required knowledge and capability and, of course, the data readiness.

Are we dealing with AI in a responsible way? Is there any GDPR for AI?

The underlying complexity makes it extremely difficult to create a GDPR for AI. Thus, it comes down to the behaviour and expertise exhibited by the AI practitioners, to separate the legal and the legitimate. As due to the intrinsic complexity it is very challenging to formulate and then to enforce a regulation, data can be used for many purposes in the realm of legality, but are these legitimate uses?

To address this gap, there have been several attempts by leading educational institutions and industry-supported institutes to provide an ethical framework around AI. Most of these though have focused rather on the philosophical side of the machines and human interaction. AI is happening here and now, so we need a practical and comprehensive code of conduct for professional AI practitioners.

4 The myth of super human AI, available athttps://www.wired.com/ 2017/04/the-myth-of- asuperhuman-ai

Chapter 2 – responsible AI

1 See http://www.code-of-ethics.org/code-of-conduct/

E.ON, together with the University of Oxford (Prof. Peter Grindrod), pioneered this area. We took a leadership position in ethically applied AI and created the Oxford-Munich Code of Conduct for professional data scientists. E.ON is embracing this code, but many other companies and educational institutions are also supporting us and implementing the adoption (to name a few, the Alan Turing Institute, Allen & Overy, PWC, etc.). Our customers can be certain that all the new solutions we are bringing to them in the solutions and networks spaces are powered by responsible AI, and it is an important message to convey.

The Oxford-Munich Code of Conduct for professional data scientists contains 1

1 Lawfulness 2 Competence 3 Dealing with data 4 Algorithms and models 5 Transparency, Objectivity and Truth 6 Working alone and with others 7 (extra) Upcoming ethical challenges


Chapter 3 – the Data.ON

transformationprogramme

“In order to embrace the full potential of data, we cannot underestimate the need for a cultural change. We need to fundamentally rethink how we use our data, how we are preparing the organisation to deal with data and how to unlock the full potential of data and AI to create disruptive products. Data.ON programme is set up to tick all these boxes.”

Matthew TimmsChief Data and Technology Officer

Overall programme set-up Defined and started a transformation programme addressing each and every data and AI strategic priority. Recruited programme team.

Data Readiness groundwork Defined the overall data management and governance strategy including the selection and contracting of the global tool, a data management and governance pilot in Romania, the consolidation of the data purchasing processes and the roll-out roadmap with the business units.

Process Mining & Intelligent Automation Established the fundamentals for the

end-to-end holistic automation, bundling process mining, process automation, cognitive services and chatbots and created the first proof of value.

Dat-A-Cademy and evangelisation events Established the company-wide Dat-A- Cademy, onboarding a learning partner, starting with 30 students from different departments across E.ON. Hosted more than 20 data events, including roadshows in the different regions.

Data visualisation Created an active Data.ON community.

Incubator Lab set-up Ramped up the bottom-up data Incu- bation Lab and delivered the three proofs of value, one of them LIVE and being exploited by the E-Mobility domain.

Local Data.ON Hubs Defined and set up the Data.ON local hub construct and implemented the first one with Sweden.

Data Architecture and best practices Created a Tech radar covering all technologies relevant for data science and engineering. Defined best practices and guidelines together with our partners.

Status summary of the Data.ON transformative activities


Focus on delivery

All data professionals share an inevitable passion for playing with data, writing code, trying out new algorithms … it’s very close to having butterflies in your stomach (a similar feeling to being in love) .... But sometimes we fall in love so much with the problem that we forget the reason why the problem is supposed to be solved and what is just good enough. Many companies employ data scientists to play with data and hope for a meaningful result with a huge impact on the business … while bottom-up innovation driven from the data can report, with the right guidance, substantial and unexpected benefits, data professionals need a proper vehicle to put the business value back in focus. That’s how we started Data.ON: focused on high-impact delivery projects, where algorithms are crafted to solve business problems with all kinds of data.

Scaling up and preventing fragmentation

Focusing on delivery, in a nutshell, means finding the right data and the right algorithms to solve a very particular – and usually well-defined – business problem (e.g. create a model to predict the lifetime of an medium-voltage cable for one of our network operators, or the likelihood of a lead turning into a sale for one of our retail business in Europa). Often, we solve the same problem over and over again. The same machine learning model is created for every country, distribution system operator and solution domain, or within the same domain and same country, once for B2B, once for B2C, once for municipalities, etc. Moreover, if we abstract away the specifics of the problem, we can reuse the same algorithm in many places and situations. For example, the same model that monitors values in a time series and alerts on a condition where the actual value diverts from the expected value more than two sigmas, can be used as an early warning system for the sensors of a wind turbine, the energy consumption of an industrial building, the yield of a photovoltaic (PV) panel, the conversion rate of our B2C online commodity sales in Germany or the stability of our grids. The same applies to data. Circa 80% of our projects require weather data (of course, with different levels of granularity, history, metrics-richness, etc.). Again, the effort of finding, purchasing/procuring and normalising data does not need to be repeated per project. But what happens if we continue „just delivering“? There is not only a risk of reinventing the same wheel multiple times, sometimes these wheels are invented using different technologies, vendors and data formats, which in the end creates fragmentation and is very difficult to maintain.

The birth of the transformation programme

As we prove value, we receive more demand from the organisation. Alongside this demand our responsibility grows, as we identify further potential value pools that are “up for grabs”, but because of bandwidth constraints, these low-hanging fruits might remain unharvested. We need a mechanism to scale up to satisfy this demand and to equip ourselves, but also the rest of the organisation, with the right value-harvesting tools. That’s why we created the Data.ON Transformation programme (presented and approved in COO-C SLT in March 2018 and by the E.ON SE Board on the 15 May 2018).

Chapter 3 – the Data.ON transformation programme

Strategic Priorities

We purposely decided not to create a specific data and AI strategy, although we are often asked about that. Rather, our aim was to define a set of strategic priorities or areas of focus with very specific activities, concrete outcomes and measurable results.

These capabilities have been defined after 6 months of consulting and interaction with various parts of the organisation and should be considerednot only the glue, but also the lever to take our data and AI initiatives to a whole new level: from punctually delivering projects targeted to make a high business impact, towards a data-driven modus operandi across all regional units, business units and functions.

Data Readiness (DARE)Data is the core of all Artificial Intelligence activities, as there’s no AI without

Fig. 3 data.ON Transformation programme strategic priorities

DataReadiness

DataEvangel-

isation

AIReadiness

IncubatorLab

ArchitectureBest Practices

& Services

DataManagement

Harnessing the power of our data to digitise our customer solutions and make a better tomorrow

AI Cababilitybuilding

Data.ONTransformation Programme

Shared Data / Algo

Data-2-Biz Value creationDat-A-Cademy

Governance &GDPR

AI StrategicPartnerships

Data Architecture

PartnersCo-Development

Data.ON Community

Data ContractsAdministration

Academia-2-industy

Best Practisesstandards

Rotation-ProgrammeData storytelling

UKData.ON

Hub

ItalyData.ON

Hub

…SwedenData.ON

Hub

DEData.ON

Hub

Local Data.ON Hubs


the proper data foundation. DARE is a very ambitious Data.ON subprogramme with the potential and the clear goal of changing the way the organisation thinks of and handles data. DARE is the main enabler to exploit the value in the data, for which we need to: • Implement a standard, systematic and efficient mechanism to identify, describe and catalogue all data sources (owned and acquired) across the organisation. • Establish “lite”, tool-supported data governance (who can access what

with which purpose under GDPR compliance). E.ON always puts its customers first and therefore we go the extramile making sure our customers‘ data is treated with the highest security and privacy standards.

• Standardise a secured infrastructure where all data is centrally stored and managed whilst also integrating the distributed data sources.

DARE has been divided into three core areas. These areas are interconnected and some of the working packages certainly require cross-area cooperation, but the separation of areas will enable the proper definition and grouping of activities and accountabilities.

Data Management Practice and Data Governance (DMP)

As the field of Data Management and Data Governance can be arbitrarily long, we have defined a crisp set of objectives we want to cover in this area: 1. To define a standard way of documenting our data assets. 2. To implement a tool complying with the standard defined in #1, where all data assets can be documented and kept up to date. 3. To catalogue all data assets available at company level (both owned and purchased data sources), documenting all data sources as defined in #1 and supported by the system created in #2. 4. To identify and document gaps and overlaps, and define a strategy/guidelines on how to deal with them. 5. To define a standard process for data purchasing to optimise reusability and economy of scale.

Data Governance

Docu-mentation

Data Operations

TechnicalIntegration

Skills &Enable-

ment

DataPrivacy & Security

Data Architec-

ture

Data QualityMgmt.

Meta Data

Mgmt.

LicenceMgmt.

Data Discovery

ConnectedSystems

DMP – Track External Data / Data Licence Management • Track all contracts for data licences (contract information and meta information) • Implement tool/data warehouse for tracking of contracts and meta- information/licence management • Implement an automated process for detection of new contracts (e.g. through SAP API) • Implement clauses for future contracts (data storage, data usage, data ownership) • Define best practices for data purchasing • Establish the central data team as a single point of contact for any Data license demand

Core Data Landscape tracking • Register all internal sources in Meta Data Management Tool, split into 2 phases:

• First phase: all sources already stored in Azure and subscriptions to Azure

• Second phase: Other sources not yet in Azure (with direct check if connection to Azure is to be conducted)

• Implement automatic source recognition with alert functionality • Register all external sources in MDM Tool • Ensure availability of data in cloud if needed • Develop APIs for data sources to Cloud

Meta Data Management Tool implementation • Implement MDM Tool for internal and external data • Ensure connectivity to Data Management Platform • Ensure connectivity to contact/license management tool

Chapter 3 – the Data.ON Transformation programme

6. To revisit all existing data purchasing contracts to find out which ones require renegotiation to optimise reuse and access.

7. To establish a standard data usability clause when we contract any service of any agency to ensure data ownership and data accessibility. 8. To break down data silos by making all external data accessible. 9. To formalise standard data models across E.ON, defining standard business ontologies and a proper nomenclature.

Data Privacy Management (DPM)

As we have done for the previous area, we also set clear goals for the Data Privacy Management:

1. To classify all existing data sources from the data privacy perspective.

In order to reach these objectives, we have grouped our work into the following working tracks:


2. To document the set of use purposes granted at data source level.

3. To implement a set of terms and conditions for using a particular data source.

4. To establish a mechanism to properly manage opt-ins at customer level.

5. To establish a multi-approval data access standard process, complying with GDPR, including roles’ definition, workflow management and tool implementation.

6. To define anonymisation best practices to make the use of personal data possible whilst complying with GDPR.

We are in close contact with our data protection department to ensure a high level on data protection.

DPM – Track Data Protection

• Develop new class structure (if needed)

• Document the set of usage purposes granted at data source level

• Implement a data source – terms and conditions

• Develop a multi-approval data access standard process, complying

with GDPR, including roles’ definition, workflow management

and tool implementation

• Develop a mechanism to properly manage opt-ins at customer

level (process)

• Classify all data sources in Azure

• Define standard anonymisation methods/guidelines/

best practice

Data Security and Infrastructure Goals that we are pursuing are: 1. To make sure we have our platform of choice. 2. To adapt the existing data onboarding process to comply with the standards defined in the Data Management Practice.

3. To document the set of usage purposes granted at the data source level.

In 2018, we carried out all the enabling work, such as tool selection (including LIVE PoCs), definition of workflows, processes and roles, federated data-governance schemas, etc. More details on the progress of this work can be found in the deep dive section.

In early 2019, we finally onboarded the first region (E.ON Romania) and defined an onboarding plan for the top regional units and energy network companies. In addition, we set the principles for federated data governance, including roles (lead data manager). At present, we are finalising a Data Governance playbook, where our federated data governance model is described in all its dimensions and shall serve as reference for the entire group.

4. To implement a set of data source terms and conditions.

5. To establish a mechanism to properly manage opt-ins at the customer level.


Data Management Platform • Implement a Data Management Platform at the centre of all tools/data warehouses and front end for customers • Mainly front end with connection to implemented tools/ workflows • Possibility to search data • External as well as internal based on MDM Tool • DPM workflow

What is Data Management? • A business practice to provide systematic capabilities to leverage and prosper the value within existing data assets as well as the understanding of the economical relevance and need to build and nurture new data assets • Data Management encompasses all stages in the life cycle of a data asset and oversees the availability, usability, integrity and security of our organisation’s owned and licensed data

What is Data Governance? • A systematic approach to align E.ON’s strategic and operational decision-making to prosper and leverage data asset values on a global scale • DG encompasses policies, standards, rights and rules on data as well as their proper implementation • It builds a clear global framework on local and global accountabilities, procedures and metrics on data management.


Find the data• Search• Access

Acquire, trade &sell the data• License & Distribute• Exchange

Manage the data• Understand impact• Measure and grow data asset value

Understand the data• Meaning• Usage• History

Trust the data• Governance• Quality• Security and privacy

Share the data• Provide access• Transfer data

data Governance

A systematic business management practice to align data manage-ment with business strategy as well as comply with growing legal & regulatory obligations

Data evangelisationThe first step in a transformation journey is often evangelisation: before you can run, you need to learn to walk, and before even walking, you need to trigger the need for it and hand-hold during the first steps. That’s the aim of evangelisation. Even if AI is hyped, not many people across the organisation are aware of the possibilities and the size of the opportunity when it comes to harnessing the power of data. Some, willing to work with data, don’t quite know how and where to start. Others, unintentionally limit the value of the gathered data by not observing basic best practices and principles.

Dat-A-Cademy: growing data scientists and engineersacross the company

The main focus of our evangelisation activities has been increasing the company’s data literacy. In 2018, we initiated our first Dat-A-Cademy, a space where our employees can take part on the best-in-class data science and engineering courses, starting from absolute beginners’ degrees to highly specialised data professional courses. The Dat-A-Cademy was successfully launched in October, and after the pilot phase, we refined the concept and are ready to offer it to the entire company.

After a thorough analysis, we contracted Udacity as our learning supplier and started a nano-degree pilot with 25 participants from more than 20 different E.ON units and 4 countries. Udacity fully integrated into our procurement platform, and now offering various nano-degrees for 3–6 months (from machine learning, to computer vision, to deep reinforcement learning and many more topics). Free courses from Udacity, Coursera and Google will function as a trial to bridge the gap to our nano-degrees. In October 2018, we started our Dat-A-Cademy pilot to shape the offering, the dynamics and the mentoring concept for E.ON. 25 employees from 6 different countries took part on this pilot. Typical big company hurdles (administrative processes, rounds of

Fig. 1 overview of Data Governance approvals, endless discussions, etc.) didn’t curb our enthusiasm to create a learning space for everyone interested in data and AI. Our initial purpose of growing data scientists and data engineers all across the organization had now the proper learning hub. We used this pilot to learn to adapt the offering to our employees and to tailor the students experience to maximize both completion rate and applicability in their daily jobs. Now we are in the scaling up phase, expecting a new promotion in our Dat-A-Cademy per quarter.

Data Storytelling and Events We’ve been to different roadshows in each and every regional unit, to ”show and tell“ the status of our delivery projects. In addition, we have hosted, together with some of our partners, different ”Data Days“ events. One of the most popular ones was the Data Visualisation Day, which which took place for the first time in November 2018 with record attendance. We experimented with a new format: we invited the two main competitors, Tableau and Microsoft PowerBI, and we let them present, pitch, coach employees and compete on a particular challenge.

At E.ON, we have an obligation and duty to show the community how data is transforming the energy world and how data and AI play an indisputable role in this transition. Hence, we have attended multiple conferences to speak about it (see table below).

With a different audience in focus, we also hosted several meet-ups for the data science communities to learn how to work with us and to get them engaged in social projects. Examples are PyData (2x) and AppliedR. These events, apart from being an excellent way of giving our team members the opportunity to present a data science challenge they are working on, are an excellent medium to find new talents and position E.ON as a reference in the data science space (targeted employer branding).

Event Location Topic

AI Expo London, April 2018 How AI is reshaping the energy world

Tech days Munich, July 2018 How Artificial Intelligence is making our energy more human

Design IT Munich, August 2018 How AI is transforming our energy world

Brain Bar Festival Budapest, June 2018 Can data change our energy and save the Earth?

SOMET 2018 Granada, September 2018 How to successfully implement AI in the industry: from hype to value

Data Leaders Day Berlin, November 2018 Ethics in data science

E.ON LecturesBehavioural Change Milan, November 2018 Behavioural mapping using smart meter data

Munich Data Geeks Munich, December 2018 A deep learning approach to data scientist capability model

Big-Data.AI Summit Berlin, 11 April 2019 Energy.AI - This is how AI is Reshaping Today the Energy World of Tomorrow

Data Festival Munich, 20 March 2019 Panel: Ethics in AI

#AISummit2019 London, 12th - 13th June 2019 AI, ML, Smart Data: New Technologies, New Opportunities for Energy



Data.ON Hubs: a global-local model of engagementIn order to organise the cooperation between the local data science departments all across E.ON, we developed the concept of Data.ON hub. A hub is a virtual community where data leaders and practitioners from different departments across a particular regional or business unit interact in an organised way with our central team. The nature of the interaction is manifold (including demos from existing projects, consultancy sessions, discussions about technology and best practices, data management, privacy-related issues, etc.). The information exchange takes place between our global team and the data practitioners in the local unit, but also between members of the local unit working in different areas, who previously would not have had this kind of exchange. It enhances explains the success and the richness of the contents and exchanges. Meetings take place in a remote way, with a predefined agenda. If a point on the agenda requires a particular skill or type of expertise, attendees from the central team are invited to join.

The interaction with the Data.ON Hub obviously goes beyond these meetings, but having a regularly scheduled checkpoint has proven to be very beneficial. Further interactions take place when joint projects emerge. The Data.ON hubs were created to maximise the re-use of data and machine learning assets across the group, to push for standardisation and best practice adherence, to sustain regular communication and exposure to the ongoing activities on both sides, and to define engagement and joint projects.

One of the immediate benefits of the Data.ON Hub construct is the increasing alignment: when there are outstanding needs of a particular regional unit, the local team can learn whether this problem has been solved somewhere else before, whether there are data and/or algorithms that can be reused/retrained for the new scenario, whether there are experts to consult on how to deal with particular difficulties, whether there is the possibility of starting a joint project, etc. The global team, on the other hand, can update the global AI roadmap and keep track of the needs/developments at a local scale, advise on and/or participate in building a solution, etc. Data.ON Hubs are planned to converge in the same virtual community. Multiple hub events take place in a less frequent manner and are conceived as a joint space, where countries or business units with similar problems are brought together by the Data.ON central team to discuss and steer the data and AI development.


Architecture, Best Practices andServicesShared data and algorithmsAs we continue to deliver an increasing number of projects, a common side effect is the duplication of work. Having a solid industrialised, well-documented, componentised set of intelligence assets (e.g. own time series analytics library) and data (e.g. curated weather data service, exposed over an API offering historical and forecasted data) can save a lot of time and effort.

We have established a whole roadmap of shared data services. Our starting point has been the weather, which is live, and now we are focusing on all the market data required for the energy economics activities. We have invested some effort creating the right technical set-up (reliability, concurrency support, latent optimisation, caching, etc.) from which all upcoming services can benefit.

The plan is to also consolidate all sources – owned and purchased ones – that are worth exposing, so that we enable many use cases across the business. Algorithms are more difficult to share. There are some examples provided by the large cloud vendors (AutoML, or the whole set of Azure, AWS or Google cognitive services), but usually, solving a real problem requires specific measures. It does not prevent us from sharing libraries, so that the next data scientist facing a particular problem can leverage all the previous work that has been done.

Fig. 4 example of Tech Radar for Data Science Tools (June 2018)


Guidance and best practicesOne of the overall functions we have adopted consists of defining and sharing best practices across the entire data value chain (data pipelines, data scientist frameworks, libraries, visualisation tools and best practices, etc.). On the one hand, this enables us to standardise across E.ON, and on the other hand, to reuse components more easily across varying business units.

We also include best practices in the way we deliver projects, write code and share assets, as well as on how to document our progress and projects. One example is the introduction of Machine Learning Canvases, which are single pages where business owners, data scientists and data engineers converge to the same definitions and goals.

Fig. 5 Machine Learning Canvas as a communication element between Data Scientists and Business Stakeholders

Technology RadarAI and data are only possible with the right technology in place. It’s crucial to make the right choices regarding technologies and to acknowledge the fact that technologies evolve and organizations need to stay up to date. For this reason, we decided to introduce the so-called Tech radar, where we classify the tools and technologies required to accomplish a particular task, into 3 groups:

• Legacy/on hold: technologies/tools we have identified as available in the . E.ON landscape, but whose usage should be discontinued in the short/ medium term. • Adopt/in use: technologies/tools that are state of the art and will be introduced (if not ”in use“) and adopted right across E.ON.

• Explore/assess: technologies that are emerging – getting strong support from the developers and/or data science community – or are already mainstream, but not widely used at E.ON, and that we should evaluate or are actively evaluating.

AI ReadinessAI Readiness is building up a global AI Governance Set-up at E.ON in order to reduce fragmentation, minimise duplication of work and optimise AI vendor and partner management. In addition, it is very important to establish E.ON-wide AI capabilities internally and reduce dependencies on external parties. For that, we have setup a Steering Committee, an AI roadmap, AI backlog management as well as organising AI capability development and strategic partnerships.

AI Readiness follows three main goals. The first is to increase scalability and reduce duplication concerning our AI projects across E.ON. It is very important to start aligning AI projects, lift and shift valuable use cases and share knowledge. Secondly, to optimise AI vendor management in a way that reduces external AI spending, consolidate E.ON-wide vendor demand and build up capabilities internally. And lastly, to efficiently build up strategic AI Partnerships – with academia and the corporate sector.

The key drivers in this workstream are governance and transparency. The project needs to create overall transparency about the global project landscape. The aim is to discover all E.ON-wide AI projects, define assessment criteria and consolidate this all into one roadmap (Dashboard). In addition, a quarterly AI SteerCo needs to make roadmap decisions (e.g. prioritisations). A capability framework matching our future use-case demands is helping us develop all the required skills. Onboarding strategic AI Partnerships and offering shared services and know-how via our Data.ON community will be established.



Concerning the AI Governance, more than 30 AI projects from Germany, Sweden and Italy have been identified and collected into an Excel sheet and pre-assessed. The backlog management process has also been defined (e.g. discovery workshop). Additionally, an AI capability metric has been developed, based on best practices from leading-edge AI companies. Moreover, partnerships with Microsoft, Google, DAA, the University of Jerusalem, the University of Nottingham and the University of Granada have been established or are being discussed.

Data Incubation LabAs a side effect of our strong focus on delivering value and engaging in high value, high visibility projects, we’ve been losing the “freedom” to innovate with the data assets we have. Nowadays, we cannot neglect the output of new ideas, and business models which are inspired by analysing our data. Thus, we decided to create a space to host bottom-up data innovation, which we call the Data Incubation Lab.

An innovation approach governed by proofs-of-valueInstead of “just playing around with data”, we define a clear process and output of each attempted innovation. As well as putting a time box on our innovation cycles, we also ensure prototypes are taken out to one or several business units, with a value ascribed to them. From there, we decide, together with our business stakeholders, the next steps to exploit the value.

Co-development space for our partnershipsWhen we initiate a cooperation with a top university, a tech company or a company from a different industry, we usually explore co-development set-ups. We have engaged with the University of Nottingham in a UK-funded project (Innovate UK programme) to explore the value of smart meter data, and have performed several technology proof of concepts with companies such as Microsoft and Google.

Keeping data science’s flame alightThe Incubation Lab is also hosting so-called “Data Labs Days”, typically lasting for 1–2 days and dedicated to a specific topic, e.g. technology that can then be worked on and researched in a hackathon-like manner. This helps with creating and evaluating ideas and technologies. Examples of Lab Days are “Text Analytics Hackathon”, Hack4Sustainability competition or several technology exploration events in collaboration with our main partners.

Lastly, we defined rotation programmes to allow our data professionals to work for multiple weeks on a dedicated lab project outside their daily delivery pressure.

1. Use-Case Backlog 2. Screening 3. Prototyping 4. Piloting 5. Scaling

BUs RUs

Prototyping at RU/BUto confirm value andtechnical feasibility

Prove results in realworld scenario withcustomers

Scale use-case toother RUs/BUs

Academic Partner

Incubator Lab

Value demand ReusabilityTime to market

Delivery capacity: Data.ONAI CoE partners. OffshoreCoE, Local Data.ON Hubs

CorporatePartner

Idea potential assessment


Fig. 6 gating process to ensure fast-failing and secure the commitment towards value

Data.ON central team

To make the most of our data assets, we have been investing in bringing the best professionals from all over the world. A central team with a very strong engineering and scientific background has benn continuously growing to satisfy the AI demand of our company. In parallel to the central team, almost every regional and business unit has onboarded data professionals in various functions, growing the community of data practitioners across E.ON.

• Average age 34• > 500 publications• 10% attrition rate• 21 job profiles

• Hiring rate 1.2 persons/month• 16 nationalities• 67% PhDs• > 15 patents


Our rolesIn order to deliver data and AI projects while transforming the organisation, we have the following roles in the team:

Our Delivery ModelThe Star Model supports the scaling up of capabilities through external partners and near-shore extended capacity teams, while minimising the risk of knowledge loss.

Project ManagersOur project managers bridge the gap between technology and business. They coordinate, balance demands and organize teams.

AI value trackerInspect all our initiatives for value and ensure the traceability and the time to value. In charge of ensuring the fast-failing of initiatives.

Data InnovatorsCurious by nature and playful with data, our innovators identify and develop new data-driven use cases wherever they go.

Big Data ArchitectsDesign robust architectures leveraging cloud platforms to ensure the scalability of all the data products we create.

Visual Analytics EngineerThrough visualizations, our visual analytics engineers are able to communicate complex ideas to a broader audience. Design works to enhance understanding with interactive data visualizations and infographics.

Data ManagersOur data managers define our customers’ data needs, develop and maintain privacy policies and standardize data collection while educating the business and ensuring they are in compliance with ethics and standards.

Data ScientistsOur data science team is drawn from a wide multidisciplinary background. They are experts in machine learning, statistics, computer science, etc. Their diverse backgrounds allows them to find the best approach to every problem.

Big Data EngineersOur data engineers are highly experienced software developers in charge of building and maintaining the company‘s data pipelines and making all data assets usable.

Data.ONRoles

Discovery Workshop1st workshop to review idea and assess feasibility

Lead Data Scientist / Engineer• Single point of contact for business team• Fully accountable and coordinating the entire “Star-set” of internal and external resources

• Responsible for project sta�ing, partner, IT and knowledge management• “Star-set” varies during project

lifetime and depends on project scope

AI Roadmap DecisionSteerCo decides on roadmap placement

Prototype & Testing

Scaling across E.ON

Piloting & Delivery

Use-Case AssessmentAssess value, demand, time-to-market reusability

Project LaunchProject set-up and start of work

Engagement process Project set-up “Star-Model”

ExternalScientist

DataManager

VisualEngineer

ExternalScientist

DataArchitect

Lead DataScientist

ExternalScientist

Product Owner

Scrum Master

Business Owner

Data.ON Team Business Team

Dat-A-Cademy What?Dat-A-Cademy is the cornerstone of our data science and AI upskilling initiative “Data Evangelisation” throughout the company. If we want to transform our organisation, we need to start with transforming our people. Therefore, we have established the Dat-A-Cademy to offer a data- and AI-centred, well-integrated learning platform as well as an expert environment for our colleagues. AI should reach each and every employee in the company.

Why?The aims of our Dat-A-Cademy are to reach every single E.ON employee and help them to embrace the power of data in their daily work in order to make better (data-driven) decisions and increase productivity. With the Dat-A-Cademy, a central access point for data- and AI-related training courses and learnings will upskill our employees in a holistic and personally-guided way.

From now on, all E.ON employees can enhance their data and AI knowledge!


Fig. 1 dat-A-Cademy roadmap 2019 from pilot to roll out

Fig. 2 Strong partnership with Udacity (Juan Bernabé-Moreno and Sebastian Thrun -founder-)


How?We have developed a lean learning platform, the Dat-A-Cademy on the E.ON’s intranet. After thorough analysis, we contracted Udacity as an ideal learning partner and started a nano-degree pilot with 25 participants from more than 20 different E.ON units and 4 countries. Udacity is now fully integrated into our procurement platform Semigator and offers various nano-degrees for 3–6 months (from machine learning, to computer vision, to deep reinforcement learning and many more topics). Free courses from Udacity, Coursera and Google will function as a trial (low-barrier) version to bridge the gap to our nano-degrees.

Result?The Dat-A-Cademy has been successfully launched, offering eleven nano-degrees (pilot) and more than 30 free courses already impacting >40 employees already. In 2019 the Dat-A-Cademy has been rolled out to the whole of E.ON, the free course portfolio has already surpassed the paid content offering and Data.ON expert coaches and topic-specific communities are engaging in expert communities and massively improving the data and AI skills in the organization.

Fig. 4 our 25 pilot participants from > 20 E.ON unitsFig. 3 carefully selected nano-degree pilot course portfolio from our learning partner Udacity

Fig. 1 E.ON employees’ travelling habits Fig. 2 time series are typically difficult to visualise

with the results of a machine learning method and our business stakeholders can only take action on our insights if we increase the enagagement. The Data Driven Documents (D3) tooling implemented by the major vendors allows for a playfull way of visualizing and „touching“ the data and at the same time, given the low adoption barrier, is used as one of the most engaging data evangelization practice.

Why?Visualising data via Tableau, Power BI or Click is a powerful method to gain fast insights and save valuable time. Its primary goal is to turn data into something valuable and meaningful, which can be absorbed and understood by non-tech people. Good data visualisations lead to faster and better decision-making with fewer distractions and less confusion.

How?We use visual analytics in almost every project. It is essential to communicate the valuable insights to the audience. Our primary tools of choice are the leading-edge software Tableau suite and Microsoft Power BI. On top of this, we programme visual analytics with Python and R or use open source tools (Kibana, Grafana, native D3.js, etc.). Result?The power of data visualisation is already included in all big projects, including: “Predictive Maintenance for Assets”, “Optimum”, “Digital Control Centre”, “Smart Meter Analytics”, “Network Zone Balancing” and many more. In the subsequent sections of this book, some of these projects are described.


Visual Analytics – the art of data storytelling

What?Data visualisations are key for presenting powerful insights generated by our algorithms. Our brains can process colour- or shape-encoded information many times faster than pure lists of numbers. On top of this, the art of data storytelling complements the process of presenting the valuable insights to the business. It is essential to guide the business audience through the large amount of data and insights. For us, data storytelling is an essential communication tool for our data professionals. It‘s really difficult interacting


Process Mining & Intelligent Automation

What?One of the fields where AI can prove immediate results is the automation and the efficiency increase of existing processes. There are already well-established technologies, such as Robotic Process Automation (RPA), to record human interaction between 2 systems and play it automatically without human intervention. Although there is not much intelligence in that, it can be very useful to decrease the overall process time. RPA is useful when we know the process, but this is often a challenge. The traditional way of just surveying employees involved to deliver a process chart is not only obsolete, but cannot be traced and monitor for improvements. Reading time-stamps, steps names and actors, it is possible to re-create a very accurate and measurable picture of any process. Simulation techniques can spot unnecessary loops, suggest the use of steps automation and keep the trace on efficiency KPIs. That’s the so-called Process Mining. There are many vendors, such as Celonis, providing a very easy-to-use integrated process mining solution. Apart from using them for large scale processes, we have created our own open-source process mining tool with increased intelligence capabilities. With that, for example, we are currently analysing the event data from smart meter installations in Sweden to gather insights about the biggest roadblocks in the installation process. This includes refining the process steps together with the business owners to determine, on an abstract level, what the process should look like in the normal case. Then the process model is inferred based on real world event data that can be compared to the expected process.

This model shows most used paths in the process as well as outliers in the process. Furthermore, it directly indicates the most time-consuming edges or steps in the process that enable successive optimisation of the process together with the business owners. Using the insights gathered by mining the data, further, the intelligent algorithms like AI-based cognitive services can be employed in combination with robotic process automation and chatbots to improve the workflow.

Why?The analysis techniques in process mining enable the organisation on the one hand to gather insights about the performance of each step and also find the biggest opportunities for optimisation. This can result not only in faster execution times but also in happier customers, who can benefit from their smart meter earlier. On the other hand, it can also be used to check for compliance to see if the standard process was always used and also to enable further steering in the organisation to reduce the costs of complying with regulations.

How?Intelligent automation consists of several modules that build on top of one another. At first, the processes that have the highest potential for improvement

are considered for deeper analysis and to understand the stakeholders, systems and interactions at an abstract level. Process mining techniques are used to automatically infer a model of the event traces to make the process more transparent and understandable from the real world. These techniques also provide ways to detect unnecessary loops and bottlenecks in the mapped-out process to identify further key improvement areas and performance decay drivers. In a next step, these areas can be sped up with state-of-the-art techniques from Robotic Process Automation (RPA), chatbot or cognitive services to assist and automate cumbersome areas in the process. Result?The first process currently being analysed is the installation of smart meters in Sweden. Further projects, e.g. for the I-MOVE customer journey, IT ticket workflows and procurement processes in finance, are currently in the onboarding phase. Process mining is especially interesting for global scale processes, where the benefits are measured in millions per year. The first global process we have address is Procure-to-Pay in the global procurement department.


Fig. 1 resource dependencies overview Fig. 2 frequency by traces

Fig. 3 process map Fig. 4 – activity distribution by step


Chapter 4 –our delivery

activities

With the advent of technologies for metering energy consumption (smart meters, smart plugs, etc.), energy storage and energy generation, the volume of data has exploded. The Internet of Things (IoT) is changing our relationship to energy in many aspects and rapid technology development keeps driving down the hardware prices almost exponentially. It does not only affect energy metering equipment, but also all kinds of sensors. Also, of high interest to us are the so-called “actuators”, or components providing steering capabilities, usually in response to sensor readings and some logic for contextual decision-making.

“Artificial Intelligence will change everything – I personally believe it will bring a new and better future for us. We are already using AI in our work today: the Future Energy Home without AI is not possible. We use data intelligence in Home Energy Management, E-Mobility, Solar, and Heating and comfort solutions. By using strong AI input we can build new business models, test new market approaches through growth hacking methodologies and much more. This is just thebeginning. In the future everything will be intelligent and we need to be at the forefront.”

Frank Mayer Senior Vice President B2C/SME Solutions, E-Mobility & Innovation, E.ON SE

Smart Home intelligence suit Combined use of smart plugs and smart

meter data to detect consumption triggers and disaggregate power

Smart Meter behavior modeler

Extracts Insights from customers to provide personalised experiences

Power Disaggregation for residential customers Enhances our understanding of customers’ needs while providing transparency about

consumption breakdowns and energy efficiency levers

E.ON Optimum solution Makes energy intelligent and simple for B2B customers applying state-of-the-art machine learning methods

Advanced Analytics for Solar Panels Provides actionable insights and improves overall solar production and intelligent PV panels operations

Summary of AI powered Energy Solutions


Anomalous behaviourConsumption, generation, battery-loading time, etc. are usually time series of numeric values. It allows us to apply all kinds of time series analysis methods to provide insights. The classical scenario consists of identifying anomalies or situations where the actual consumption differs from the forecasted

This emerging ecosystem presents the best conditions to apply intelligent algorithms on top of the data, to not only generate valuable insights about consumption, generation, etc. but also to steer actuators and modify the state of systems. The most popular use cases cover following functionalities:

Transparency and awarenessNo matter whether we talk about the energy consumption of a device, a home, a public building, a production plan or a municipality, a high-quality, continued and up-to-date consumption data stream is rarely available. As soon as we have a metering device, it is possible to enable a full overview of the consumption. Usually, smart meters provide values for the whole consumption of a house, a shop, a bakery, etc. In principle, it is useful information for our customers. We are applying advanced statistics and state-of-the-art machine learning methods (convolutional neural networks) to break down the energy consumption of different household appliances (fridge, washing machine, oven, etc.). Depending on the data frequency (seconds), we are able to detect events (e.g. washing started), or just break down statistically the energy usage leveraging ground truth data and household profiling information. In addition, understanding consumption often requires putting it into some context. We enable the similar home comparison functionality to allow users to understand whether their consumption is different from homes with similar profiles and by how much.

The Home Energy Management Solution (HEMS) is a great example where transparency has been successfully implemented for Photo-Voltaic (PV) panels, batteries, Electric Vehicles (EV) and further appliances, running on the state-of-the-art highly secure Microsoft Azure Sphere solution.6

6 Available athttps://www.eon.com/de/private-customers/home- energy-management.html

Chapter 4 – our delivery activities

7 Available athttps://www.eon.com/de/geschaeftskunden/ optimum.html

8 Seehttps://sightmachine.com

consumption by a particular amount. It is easy for regular consumption shapes and usages such as those used by production plants, retail shops, etc. We developed a state-of-the-art algorithm which leverages Dynamic Time Warping (DTW) and Long Short Term Memory (LSTM) neural networks to provide outstanding accuracy (paper under submission). This algorithm is, for example, in place in the Optimum solution for B2B customers.7

When it comes down to detecting outliers for a residential customer, the complexity increases, because people can behave in different ways. In order to address that, we cluster similar days (as many clusters as required), and we then assign a particular day to one of the clusters. Each cluster is characterised by the same load profiles, with a lower and an upper band. If the consumption falls outside these bands, then we flag an outlier. Outliers can reveal extraordinary situations that explain energy consumption variations but also pinpoint anomalous activity triggered by behavioural changes.

Root-cause analysisUsually, when a performance decay pattern manifests, or a massive consumption driver is identified, users expect to get hints on what the root cause for this behaviour was. With the right data in place, machine learning models can be trained to point out the drivers for exceptional situations. One year ago, E.ON acquired a company called SightMachine8 which leverages AI and advanced analytics to provide energy management solutions to manufacturing customers. Root-cause analysis is the killer application in this context.

Strategy recommender and optimisation modulesAfter identifying situations where the energy consumption can pose some risks (too high, expensive peaks, etc.) that might impact the energy targets of our customers, it is possible to recommend strategies to optimise consumption, energy costs or even both. Depending on the goals and the existing set-up (e.g. availability of flexibility, onsite generation, storage, etc.) and the frame conditions (comfort thresholds, preserve high-consumption bands, etc.), our algorithms can suggest actions depending on the envisioned impact. If the problem is properly documented, it is even possible to address it with a reinforcement learning approach.

Behavioural mapping and personalised experiencesSmart meter data (after the user’s consent) allows for modelling the behaviour of the people in a home: vacation periods, out-of-home periods, wake-up time, sleep time, time leaving the house, nights out, etc. Additional appliances, such as smart plugs, can even measure TV hours, etc. This data ensemble enables the creation of behavioural profiles – a door-opener for countless use cases based on personalisation, where customers can really engage with energy in ways that have not been explored so far. It will definitively change the relationship we have to energy and, consequently, give the energy transition a substantial push.


Advanced insights at Optimum

What?Nowadays, many B2B customers, for example large retail chains, possess various IoT devices such as smart meters, which record the energy consumption of each store individually. Applying the proper machine learning algorithms to this data, we can extract valuable insights for the Energy Analytics to steer their portfolio, resulting into significant saving at store level. In the project Optimum, we have built self-learning algorithms and end-to-end tools that enable the energy efficiency analysts to make use of the insights generated.

Why?Most retail buildings are equipped with smart meters recording the energy consumption for the whole site and/or individual devices (e.g. HVACs, fridges) or even sub-areas (bakeries, restaurants, coffee shops, etc.). Used in the right way, such data can provide actionable insights which lead to significant energy savings. For a single building, we have estimated that a efficiency engineer is in a position of reducing the overall energy consumption by at least 10%. For example, a supermarket chain from Germany or the UK, where each individual site consumes at least hundreds of thousands of kWh/year. Over the entire portfolio of sites, these insights have a non-negligible economic and environmental impact.

How?We have developed end-to-end algorithmic-driven pipelines which exploit data mining, machine learning and artificial intelligence techniques to provide energy savings insights on top of the incoming data (the smart meter recording the total electricity consumption of a building). The delivered version includes an API to consume the insights in existing front-ends, but also a Tableau based front-end, optimized for the Energy Manager persona.

Fig. 1 portfolio’s Building parameter analysis with embedded anomaly detection

Result?The following algorithms have been implemented, tested and extensively evaluated: Hourly Time Series Forecasters, Building Anomaly Detection, Dynamic Time Warping Clustering, Building Parameter Analysis, Electric Heating/Cooling as well as a Savings Estimator for Lighting. Moreover, we have implemented the complete data pipelines and interfaces for the Building Parameter Estimation and Anomaly Detection modules and created re-usable components to scale these insights in further products.

Fig. 2 overview of savings simulator for a particular building

Smart Meter Analytics and Disaggregation

What?Smart meter data opens a new door to services and allows us to extract unprecedented insights about the customer, the consumption of power and home appliances. As a first enabler, we provide insights on the power consumption by disaggregating the total power into specific appliances like a refrigerator or into consumption categories like housekeeping. In addition, benchmarking a customer with similar homes provides our customers with hints to materialize the savings potential. Further, anomalies in the consumption can be detected with high accuracy, enabling services for forecasting, optimisation, elderly care or identifying appliance defects. The framework will be extended for multi-commodity ( gas, water, heat ), enabling alarms for home protection even during vacation or office closing times in case of defects, but also alerting about base line consumption increase, periods of unusual low or high activity, etc.



Fig. 1 energy disaggregation algorithm detecting washing machine activity

Fig. 2 anomaly detection by time series prediction (blue) compared to real consumption (orange)

Why?Customers expect to get transparency on their cost and energy saving potential as a core service from their supplier, so innovative services will make our products attractive and increase customer satisfaction. Add-on alarm functions will allow new business opportunities, where a subscription model to an alarm with a regular service fee will provide new income for the business units. Developed as a platform service, the solution can be scaled over the regions and realise synergies. Having access to the smart meter data, we will evaluate innovative product propositions in proof of concepts.

How?We provide an analytics-as-a-service platform approach hosted on our Azure cloud. Smart meter data and anonymised customer information is ingested from the regional units. Business units integrate the results via API calls into their business applications, e.g. on websites or mobile apps. Currently, we support (daily) batch data ingestion, but the service may be extended to real time. The solution is implemented in Python and Spark on Databricks through training consumption models with machine learning on the historical data. In our lab, we managed to obtain higher resolution data (10 seconds instead of the usual 15 minutes) and explored the usage of Deep Learning technologies

to extract more detailed insights. Applying a combination of feature engineering techniques (such as Log-difference, Normed signal, SD of difference, One-hot value, etc), we trained a Convolutional Neural Network and we managed to detect washing events, washing cycles and washing programmes with different degree of accuracy.

Result?The analytical platform has been set up and modules for disaggregation and benchmarking have been deployed to the platform. Big data pipelines for E.ON See (UK) are up and running and the services are being integrated in the business application. Advanced anomaly detection modules are in develop-ment and being tested with Smart Meter Data from Germany for private and municipal customers. Finally, we have developed an explorative visual analytics tool to deep dive into anomalies and to support proof-of-concepts. Smart Home Intelligence

What?We are developing data-driven solutions for the E.ON smart home product proposition. It is planned to provide households with smart plugs and an energy home app that provides several services, e.g. it shall warn and notify people when an appliance (e.g. iron, TV) is running for too long. To this end, we are developing state- and appliance-detection algorithms.


Fig. 1 consumption overview based on smart plugs

Fig. 2 efficiency recommendations taking into account local flexibility


Why?The smart system will provide more insights on the consumption of household appliances, but also bring an additional level of safety by detecting anomalies. Furthermore, it will provide users with hints about how to save energy and information about how efficient their appliances are compared to other smart home clients.

How?Smart plugs measure the instantaneous power consumption of an appliance. We develop algorithms for detecting when the appliance is switched on or off and determine the consumption during its usage. We use this information to predict future values and in order to illustrate the findings we use Tableau dashboards and Shiny for testing purposes before integrating it in the final product.

Result?We have an initial version of a state-detection algorithm that can detect when an appliance has been turned on or off. This was also used to design an exemplary Shiny App that represents some of the use cases in a smart home. Via this app you can gain insights into the usage statistics of your appliances, but also receive warnings if, for example, your iron is running for too long and there is a risk of fire.

Fig. 3 assisted presence simulation Why?Analytical modules can provide actionable insights, such as the detection of under-performing sites, and can therefore trigger measures which can be taken in order to improve the overall solar power production. The advanced analytics module includes the automatic identification of those sites that are under-performing by also taking into consideration a number of factors that can affect a panel’s performance. In addition, as the project advances, we plan to estimate the value associated with required actions (e.g. by changing the inverter) by exploring recommendation techniques associated with a gain-loss function.


Advanced Analytics for Solar Panels

What?Nowadays, solar panels have become a relatively affordable asset for many residential customers in Europe. While the electricity production of such assets is heavily dependent on the weather outside, there are multiple additional factors that can influence it, too. Our goal is to develop analytical modules that can provide actionable insights, allowing measures to improve overall solar production to be taken.

Fig. 1 map showing solar plants with underperforming panels

Fig. 2 list showing the panels with anomalies and the magnitude of the anomaly


How?The core part of the product is provided by the algorithms we have developed. The algorithms are able to accurately forecast the energy production of a solar panel and compute the performance estimator for each panel. Moreover, they are able to predict the root cause of the eventual performance individually. On top of these algorithms, we provide a simple and intuitive way to check the hypothesis related to solar production performance.

Result?We have built the algorithms and the app that enables an analyst to get an initial insight into the production of individual sites and compare the production of a site to similar ones (in kWh). It also performs outlier detection using the Principle Component Analysis method. With this technology, E.ON can provide a proper service to all PV customers in UK by anticipating issues, alerting about potential performance decay and finally optimizing the maintenance cycles.

Intelligent Asset Management – the iQ-CHP case

What?Exploiting our AI and machine learning expertise and capabilities, we are building an Intelligent Asset Management solution to monitor valuable assets such as wind farms, photovoltaic panels, CHPs, EV charging stations and more. Leveraging our experience in predictive analytics for wind turbines (PredATur), Optimum and Energy Trading, we want to optimise asset availability, efficiency, profitability and lifetime, as well as improve maintenance planning.

Why?Valuable assets need to be continuously monitored. Any unexpected failure or under-performance can cause financial losses, due to unavailability and asset damage. Inefficiency and premature degradation can lead to increased costs for the asset owners with negative consequences to the end user as well. To

Fig. 1 combination of health indexes vs downtime impact for an industrial CHP

minimise this cost and optimise the asset lifetime, efficiency and profitability, an Intelligent Asset Management tool is needed, exploiting the data at its best through AI and machine learning techniques. Furthermore, we challenge the entire way assets are currently used and managed. This results in better meeting customer needs through load forecasting, increasing profitability and supporting the grid through the trading of over-produced energy.

How?The Intelligent Asset Management tool uses massive sensor data collected from the assets and sent to the cloud, where our algorithms are hosted and deliver valuable insights on the monitored asset. The results offer different levels of abstraction, from overall asset health to individual sensor information, helping operators (front-end visualisation) with its maintenance and operation.

Result?PredATur, Optimum and Energy Trading are current projects providing a strong basis for the Intelligent Asset Management product. We are in the process of acquiring leads for monitoring additional assets such as charging stations, PV panels and CHPs. We successfully concluded a proof of concept related to the application of our digital assets to the automation and performance optimisation of combined heat and power plants (CHPs), together with the Industrial Generation team (E.ON Energy Projects) within the iQ-CHP project. Following a workshop on a potential collaboration with the Global Advanced Analytics and AI team, additional areas of interest have been identified: Advanced Energy Management and Customer Production Intelligence. The former is looking into the possibility of optimising the market’s surplus energy production from the power plants using different channels. Such as a spot-market or ancillary-reserves market providing services for network stability. The latter aims to increase the flexibility and the market potential, by implementing smart power production schedules, taking into account the customer demand and its potential flexibility.

Fig. 2 asset’s health index computation overview



Chapter 5 – intelligentnetworks

“Artificial Intelligence is proving to be a key component in making our grids more efficient, smarter and able to tackle critical issues in the energy transition. Advanced forecasting methods are enabling our infrastructure to anticipate renewable energy feed-in and explore ways of increasing the renewables footprint. Algorithms are making the management of our assets all across their life cycle much more efficient and massively contributing to securing the energy supply to our customers (predict ive maintenance), and at the same time, we can better and longer utilise our assets which positively influences the network cost for the benefit of our customers.”

Markus KauneVP Energy Networks

Predictive Maintenance for networks assets LIVE in the major German DSOs for mid- voltage cables. Being rolled out and improved for further DSOs. Substation model under development. Low-voltage cables model started with Swedish data.

Local Balancing MVP ready at SIMRIS (Sweden) to predict

and steer renewables generation, storage and demand to optimise both grid dependency and generation profit.

Energy Intelligent Coordinator (ENKO) LIVE forecast prediction algorithm to anticipate bottlenecks on the grid due to renewables in-feed.

Energy Monitor Network monitor to visualise small-scale energy production and consumption with a special focus on renewable energy. LIVE for test municipalities and roll-out planned.

Within energy networks we are providing the infrastructure for the new energy world. We manage grids at high, medium and low voltage levels. The total length of our power grid after the Innogy deal is now ca. 1400 thousand kilometers and the gas grid ca. 216 thousand kilometers.

Power Networks and Smart Agents


In the Energy Networks we can clearly see three big trends reshaping the energy world: decentralisation, decarbonisation and digitalisation, with digitalisation enabling the former two. Decarbonisation manifests across the whole energy value chain: generation, transportation and consumption. In Germany alone, the installed renewable capacity (wind and solar) has increased by a factor of 20 from 2000 to 2017. It introduces new challenges but, at the same time, creates new space for opportunities for first movers.

Power grids need to accommodate increasingly higher amounts of energy generated by small- and large-scale wind-farms and solar plants. Renewable energy sources are volatile, intermittent, and to a large extent unpredictable by nature. Power grid operators are struggling with conventional approaches to deal with this situation. In addition, the coordination between renewables and conventional power plants is experiencing a substantial increase in complexity. A higher likelihood of network congestion and supply/demand imbalances requires advanced forecasting possibilities.

Another important aspect of securing the supply is the reliability of our infrastructure, an area where we have successfully leveraged our AI capabilities with quantifiable benefits.

Dealing with the renewable feed-inConnecting renewable capacity to the grid is challenging. Renewables have intermittent generation profiles, which poses unique challenges for grid balancing and reliability. Installed renewable capacity is growing at a faster pace than the pace at which our grids are able to expand. As a result, grid bottlenecks are occurring more often due to periods of high renewable feed-in. To ensure energy network stability, conventional generation plants will be shut down primarily (so-called re-dispatch). But often, this is not sufficient and renewables also have to be curtailed. This so-called feed-in management has reached such a frequency, that due to the increasing costs, related costs a new solution needs to be considered.

Advanced machine learning algorithms can provide predictions for future feed-in events and flexibility demand every 15 minutes for every substation in a relevant region, thereby giving flexibility to providers and consumers to handle relevant situations and prevent expensive interventions.

Assets intelligenceThe grid business is fully reliant on the reliability of the infrastructure. Grids are ageing and operators have to continuously make investments to renew the assets. Given that the maintenance budget is limited, it is critical to replace the right assets, based on both the likelihood of breaking and the criticality in the event of a failure.

Chapter 5 – intelligent networks

Machine learning approaches trained on historical disruptions data can support the assets replacement strategy enormously. That has been our focus in the predictive maintenance project, where we have predictive models for mid-voltage cables and are extending these to low-voltage cables and substations.

Another usage of AI technology in this area relies on managed drones to take photographs of critical networks’ positions and then use image classification to identify the position where a failure is likely to develop. Satellite images analysis is extensively used to plan vegetation trimming work in areas where overhead cables might be impeded by trees, etc. In addition, bio-inspired machine learning models can assess the degree of growth given the climate, soil composition, etc for a particular vegetation type.

Pushing decentralisationE.ON is heavily researching insulated set-ups, where a particular community can optimise its own generation, storage and consumption in order to decrease or eliminate their dependency on the central power grid. To achieve this, forecasting capabilities are essential, especially if generation relies on renewable sources. Intelligent agents can optimise the whole system depending on different objectives: maximising self-sufficiency, peak shaving, arbitrage pricing, congestion management, energy system optimisation and more. What is of particular interest is how AI governed actions translate into the physical system.

Providing transparencyWe are in the middle of the energy transition and a lack of transparency often makes us unaware of the progress. Knowing how much renewable energy is produced in relation to all other sources, understanding the main consumption drivers, gaining an insight into how these changes over time and predicting future generation and consumption can certainly improve this situation. Our algorithms support this overview, estimating generation and consumption where metering possibilities are available and providing all forecasting functions.

“The probability that we can predict a defect in the power grid has increased by a factor of two to three, and our customers benefit as well because possible sources of error that we identify in advance reduce the number of faults and make our grid more stable. (…)

Dr Thomas KönigCOO-N E.ON SE, on Predictive Maintenance


Energy Network Monitor

What?The Energy Network Monitor visualises small-scale energy production and consumption with a special focus on renewable energies. This includes, among others, solar and wind energy as well as different categories of consumption.

Why?Until smart meters are more widespread, we will not have access to real-time data for energy consumption and production. To mimic real-time data instead we visualise predictions that are – in the case of solar and wind energy – based on historic data and weather forecasts.

How?The predictions are made on an installation basis. For each installation, the measured values for the past 30 days are loaded together with the weather data for the same period. For both cases, in order to predict solar and wind energy levels on a daily basis, different models are trained and scored against each other. The model with the best score will be used for predictions.

In the case of solar energy, the models are different manifestations of a synthetic profile that is corrected by a weather-dependent factor, based on a regression model. For wind energy, we opted for a polynomial model using wind speed. This approach is useful because the relatively simple models are cost-efficient. Secondly, the daily training gives us the flexibility to react to the addition or removal of installations while maintaining a sufficient level of accuracy.

Fig. 1 network Monitor in action showing generation and consumption for a community

Result?The first milestone was the original go-live in early July 2018, including a first model for solar energy.

Wind energy was added later and both have been refined after the initial experiences. A current focus is the implementation of a new weather forecaster to improve the accuracy of forecasts.

Fig. 2 own generation vs. consumption overview over past periods

Predictive Maintenance for Network Assets

What?Predictive Maintenance is the first project where we used machine learning to predict if an asset of the electrical network is likely to fail in the near future. With this information, we optimise the asset replacement strategy to minimise the occurrence of failures. This helps spend the budget allocated by the Distribution System Operators (DSOs) for their asset replacement effectively. With our algorithms, we help to select assets with the highest risk of failure, on one hand, and components that would have the worst consequences in the case of failure, on the other hand.

Fig. 1 performance overview (a) and percentage in 2% region (b) of all 4 German DSOs for MV cables



Why?Machine learning applied to data from electrical assets provides a powerful tool to prioritise asset replacement which is more precise than conventional strategies. Different data sources, i.e. geological information, asset data, outage data, switch status, grid load data, maintenance data and weather data, are combined to automatically find the best replacement strategy. The results from machine learning are fed into a replacement strategy tool for visualisation of the electrical assets to be exchanged first, along with their locations. This approach facilitates better planning for the replacement of electrical assets and saves costs in two ways: (1) more failures are avoided and (2) less early replacement of electrical assets.

How?For our machine learning models to predict asset failure (cable) we use various properties, e.g. their electrical environment, the network topology and the specifics of an outage. To achieve this, we use historical outages and detailed asset information to train the models. We investigate various kinds of models to give optimal predictions, mostly focusing on decision tree-based ensemble estimators, which give a good compromise between variance and bias in our highly imbalanced data. An important point is to continuously test the data quality and consistency of asset/network data, which we monitor with various visualisations and metrics. We also developed several ways to quantify and depict the results and performance of our models, including classic statistical metrics (ROC curve, precision recall, AUC) and specific visualisations (replacement list results).

Result?We developed and industrialised medium-voltage cable predictions for two our our network operators, SH Netz and E.DIS, including visualisation tools to depict outages, e.g. on a map or with respect to certain parameters. The solution has been fully operational at SH Netz for over a year and created a positive impact and business value. We recently finished pre-studies of MV-C models for Avacon and Bayernwerk, and reduced the yearly variance of our models. The first models to predict substation failures were studied and are currently being optimised and finalised

Fig. 2 SH-Netz MV cables coloured by estimated replacing order ranking

Why?The curtailment of renewable energy generation facilities in the context of feed-in management and the associated costs have risen sharply in recent years. At the same time, Germany aims to reduce CO2 emissions by 80–95% by 2050 compared to 1990 levels. ENKO is aiming to reduce bottleneck management costs by partially avoiding costly feed-in management and using more cost-effective flexible usage instead. This means that a flexible consumer consumes more electricity in times of bottlenecks or a flexible feed. In addition, ENKO offers providers a non-discriminatory, simple and transparent interface.

How?Our job in ENKO has been developing the NLS “Netzleitsystem” module. The purpose of the NLS module is to make a prediction for future feed-in events and flexibility demand on a 15-minute basis for every substation in a relevant region. Based on solar and wind feed-in forecasts, a prediction is derived, which serves as an input for further processing in the ENKO Platform. The NLS model currently consists of two parts: a) a prediction module, implemented in the Azure cloud, including an automatic data pipeline and an automatic model pipeline; and b) a publicly available network traffic light functionality visualising the results of our prediction.

ENKO – predicting grid bottlenecks

What?ENKO stands for ”Energy Intelligent Coordinated” and is a digital smart market platform. ENKO enables the integration of local renewable energy more efficiently into the electricity grid. ENKO is being developed within the research project NEW 4.0 (Norddeutsche EnergieWende 4.0) in Schleswig-Holstein and is expandable nationwide. The core component of ENKO is the coordination of information between supply-side network operators and supply-side providers. It enables the different players to come together very efficiently based on their flexibility needs and offers.

Fig. 1 publicly available network traffic light visualisation showing forecasted flexibility demand



Result?As of summer 2018, companies can test ENKO in a live simulation to see if renewable energy can be used in their own production processes, which helps reduce congestion in the electricity grid. In the front end, we have NETZAMPEL, Grid-Portal, NLS-Portal and FLEX-Portal for different purposes. In the back end, we have three models running. This includes one advanced model and one baseline model forecasting the feed-in events for transformers, alongside one advanced model forecasting the overload megawatts for lines and transformers in the energy network.

Fig. 2 forecasted flexibility demand

Local balancing

What?The Local Energy System (LES) in Simris, Sweden, confirmed that it is technically feasible to manage and operate an energy system with only renewable generation, while fulfilling customers’ requirements for a stable and reliable energy supply. Based on this pilot project, we are building a Local Balancing solution for customers owning renewable generation and storage capabilities. Through generation and load forecasting using machine learning models, our customers will manage and optimise their local energy balance.

Why?With future mass decentral generation, fluctuations in generation and demand will increase the stress on networks, requiring active balancing efforts to preserve network stability. Currently, the system balancing responsibility lies with the TSOs and DSOs, but this is likely to change with increasing decentral generation. In addition, increasing local energy demand in urban areas is putting more pressure on networks. All these aspects make the capability of the local balancing of supply and demand more important.

Fig. 1 generation (PV and wind)/demand forecasting for the MVP set-up

Local balancing will help to mitigate grid congestion, but also provide ancillary services to the DSOs, thus enabling a more efficient and granular control of the grid.

How?We have built an intelligence module enabling the smart steering of local energy systems based on defined optimisation objectives. Optimisation objectives will include maximising self-sufficiency, peak shaving, arbitrage pricing, congestion management, energy system optimisation and more. The aim is to provide a toolbox with different optimisation solutions depending on our customer needs. The intelligence module is based on machine learning models, which provide forecasts on essential quantities (e.g. energy consumption and generation, storage profitability, market prices). These ML models are trained on data from the local energy system and external sources (e.g. weather data). Forecasts from the ML models are then utilised for control signals that determine the energy exchange between the external grid and the best steering strategy of the local storage systems.

Result?Based on the experiences and competence developed during our piloting of a Local Energy System (LES) in Simris, Sweden, the decision to develop the MVP Local Balancing as a part of the Transformative Platform was taken. To commercialise and deploy LES at scale, further development and the standardisation of asset communication control is needed. To enhance and customise the solution, intelligence and optimisation features are being added in a completely innovative way, extending the capabilities and the function-alities of the LES solution. A Local Balancing minimum viable product is on track, to show the feasibility and the potential of the project as well as to generate new customer leads.



Chapter 6 – Energy

Economics

“Energy Economics provides a great business field to harness the power of data and introduce algorithms. Algorithms can sense changes in market fundamentals, predict price trends and support the better forecasting of energy demand, accompany our hedging strategies, etc. We have recently entered into a journey to start realising this potential with promising initial results and I’m looking forward to taking it to the next level and scale it to all E.ON countries.”

Sven OttenSVP Energy Management

“Data Analytics and Artificial intelligence are playing an increasingly critical role in our customer business. From managing customer churn, retention and acquisition to optimising energy economics and creating intelligent bots for customer service, the room for improving the way we operate is huge. In 2018 and 2019, we made good progress in implementing the first solutions in energy economics with Data.ON. We are very much looking forward to intensifying this work in the next years and bringing real positive effects into the business.”

Dr. Victoria OssadnikE.ON Energie Deutschland GmbH


Energy Economics

Energy Economics (hedging and trading) is all about risk management. In general, the difficulties related to the proper accounting of risk-return trade-offs imply that the value pools are kept untouched or that returns are generated by taking a much greater risk than is necessary. With the rise of renewable energy generation, additional risk factors are added to the equation. The volatility of the resources makes the volume of energy generated hard to predict and puts power transmission networks under stress. This can generate imbalances between demand and generation which need to be compensated for, and that can increase the volatility of the energy markets. Better risk management and improved trading and hedging strategies can be achieved by means of several AI-driven core components within a broad project roadmap, in which the ultimate goal is to maximise the opportunities of energy economics-related strategy definition and execution.

From short to long termThe current roadmap is subdivided into different projects with activities involving several regional units (Germany, UK, Sweden and Italy). An Energy Economic Integrated data platform is being built to power different forecasting modules and enable prompt market insights. In the initial phase, intelligence capabilities will be built to: (1) improve renewable energy forecasts; (2) improve energy demand and energy price forecasts; (3) predict the overall network imbalance defined as the difference between production and consumption over time which in turn can lead to in-feed managements and to the activation

Fig. 7 Trades over time and quality ratio

of generation reserves significantly affecting energy prices; (4) predict in-feed management events, potentially affecting the production of renewable assets and increasing trading risks. This is planned to be complemented by: (5) active market news monitors, fetching, translating and assessing the potential market impact of energy-related news from several sources; (6) trading and hedging an intelligent KPI monitor, aiming to proactively identify patterns related to critical market situations. In a second project phase, automatic trading and hedging recommendation systems will leverage the market outlook generated by the various forecasting modules.

Incremental delivery with proof-pointsThe delivery roadmap comprises of a set of subsequent regional pilots which are then industrialized and roll-out to the full Energy Economic country portfolio. This strategy allows to decrease the time to market and to increase the desired impact on the full E.ON energy economic landscape.

Chapter 6 – Energy Economics


Chapter 7 – renewables and

smart Assets

Dynamic Yield Optimisation Reinforcement learning approach to minimise the impact of wake on wind parks and increase the overall energy production

PredATur: Predictive Analytics for Wind Turbines Enables the monitoring of wind parks in a scalable technology-agnostic manner.

Renewable energy sources are playing an increasingly important role in the global energy mix. In the last few years wind energy has been one the fastest growing sources of power production. In this framework, large demand has originated to reduce the cost of operation and maintenance as well as to improve the efficiency and the availability of the assets. With a constantly increasing asset portfolio (the E.ON Climate and Renewables (EC&R) wind-parks fleet currently comprises of about 150 wind-parks, located in eight countries worldwide, totalling more than 3.5k wind turbines), automated monitoring and alerting systems become critical for a timely response to failures and proper maintenance planning. As part of the Innogy deal, most large-scale renewable generation assets have been transferred to RWE, yet the AI cases are worth while presenting. Within the Global Advanced Analytics and Artificial Intelligence Unit, and thanks to the data richness provided by modern Supervisory Control And Data Acquisition systems (SCADA) installed in most of the wind farms, unique opportunities arise for the development and application of Artificial Intelligence algorithms, both for monitoring and predictive maintenance tasks as well as for asset optimisation.

Assets health monitoringThe condition monitoring and predictive analytics of wind turbines is of increasing importance as the size and remote locations of wind turbines grows. Unexpected faults, of either large and crucial components or auxiliary, can lead to excessive downtime and cost because of restricted turbine accessibility. The automated and regular monitoring of the turbine’s condition is essential to reduce unscheduled downtime and improve asset availability and efficiency. In collaboration with the former E.ON EC&R department, we developed PredATur (Predictive Analytics for wind turbines), a product that enables the monitoring of wind parks in a scalable, technology-agnostic and innovative fashion. On a daily basis PredATur monitors the health status of approximately 2,000 wind turbines and their main subcomponents. Full fleet roll-out is progressing and new sites are regularly included based on their SCADA data availability and park relevance in terms of the total annual energy production.

As part of the former EC&R digital strategy roadmap, we proposed the integration of the tool with the maintenance planning process, considering weather and sea conditions influencing site accessibility, as well as expected energy prices, to further optimise field inspection and maintenance work, as well as spare parts inventories and procurement (iPredATur).


Dynamic Yield OptimisationIn addition to the predictive maintenance tools, wind park energy yields can be increased by means of smart asset management strategies, aimed at reducing negative aerodynamic interaction (wake effects) between the turbines in a wind park. While wake mitigation measures are incorporated as part of park layout optimisation in the pre-construction phase, by maximising the inter-turbine distances along prevailing wind directions, along less frequent wind directions, residual wake effects are responsible for power production losses of up to several percentage points.

With the dynamic yield optimisation project, we are aiming to reduce residual wake losses by coordinating the control operations across all wind turbines in the plant. Currently all turbines operate in a greedy mode, trying to extract as much power as possible from the wind and neglecting shadowing effects on nearby turbines. With our approach, we help turbines to develop a “socially aware operation mode”, thus optimising the park’s overall power production.

The strategy builds upon Artificial Intelligence’s cutting-edge developments and is designed to be fully data-driven and adaptive: without human intervention it is able to react to environmental and operational changes. Prior to their deployment at the site, algorithms have been extensively tested in simulated environments relying on digital-twin models of the wind park.

According to the final pilot result analysis, implementing the algorithm across additional wind parks will be considered.

Chapter 7 – renewables and smart Assets

PredATur: Predictive Analytics for Wind Turbines

What?Predictive analytics is launched at wind parks to increase asset availability, optimise maintenance and workforce scheduling. With the increasing share of renewable energy from wind parks, efficient and reliable wind turbine condition monitoring and optimal maintenance schedules are the main drivers for increased asset availability and profitability. PredATur (Predictive Analytics for wind Turbines) enables the monitoring of wind parks in a scalable, technology-agnostic and innovative fashion.

Why?High O&M costs, unexpected wind turbine downtimes and premature degradation are the main obstacles towards cheaper and more reliable wind energy. We developed an efficient and reliable predictive maintenance tool to tackle these issues, named PredATur. Based on the value estimates for PredATur detections in 2017, the EBITDA impact is estimated to surpass several million euros a year.

How?PredATur exploits wind turbine sensor data using two complementary monitoring strategies, a park-average approach and a machine learning (ML) approach. While the former monitors the turbine status by comparing sensor readings with those of neighbouring turbines, the latter builds upon a digital twin of the wind turbines, in which each sensor output is modelled after the environmental and weather information as well as on inter- and intra-turbine sensor information. Information from both methods can be used to refine detection alerts, increase sensitivity and prevent false warnings.

Fig. 1 overview of the wind parks fleet for PredATur roll-out


Result?PredATur, developed in collaboration with EC&R,is currently live, monitoring around 1,800 turbines. A third version of the software was recently released with a retraining concept, faulty sensor mitigation, new storage strategies as well as improved pipeline performances. The project is currently focused on two additional features: a turbine health index, improving the software scalability by providing a single KPI of the turbine condition; and an automatic wind turbine curtailment detection. This will help to better label the wind turbine data for multiple post-use and preprocessing steps.

Fig. 2 health index and sensors monitoring for alerting on anomalous conditions

Dynamic Yield Optimisation

What?Dynamic Yield Optimisation (DYO) aims at increasing the energy production of wind farms by managing wake effects. Currently turbines operate greedily, trying to extract as much power as possible from the wind, neglecting shadowing effects (wake) on downstream turbines. The goal of DYO is to maximise the total output of a wind park instead of that of individual turbines.

Why?Wake management has the potential to increase the annual energy production of a wind park. This will not only increase the revenue from the wind park fleet, but will also enable the commercialization of the generated IP. It is estimated that DYO has the potential to generate EBITDA of several millions when deployed to the whole E.ON fleet.

Fig. 1 the Champion Wind Farm where DYO is being tested

How?DYO is a software package developed in-house that implements a customised Reinforcement Learning (RL) algorithm. In essence, RL is a guided trial-and-error approach, where the system learns by itself what actions deliver the best outcomes. Recent successes of RL include DeepMind’s algorithms that consistently beat humans at video games, chess and Go. In our case the algorithm receives wind and power generation information directly from the wind park in real time and selects the best turbines’ configuration for the next forecasted wind conditions. Feedback, via new power output measures is provided to the algorithm so that it can learn the outcome of the actions that it took, improving its actions over time.

Results?After showing the potential of this approach in simulated environments, we deployed DYO into the Champion wind park in Texas (USA) for the pilot phase. This pilot phase includes three groups of turbines, totalling eight turbines, out of which three are actively controlled. The pilot phase is currently running and is showing positive results.

Chapter 7 – renewables and smart Assets

What’s the current wind condition?

Evaluate the result

Select curtailmentlevel from optimal

policy

Select onerandomly

Apply thecurtailment

Select curtailment with the highest reward for the

expected wind condition in the next time frames

Goal: Find best wake management strategy

Is the reward function-reliable?

Are theremultiple options?

Yes

Yes

No, not enough information yet

No


Chapter 8 – Growth Hacking &Customer-related

Analytics

Peter Ilyés

CEO E.ON Italy

“There is no doubt that today’s world is ever more accelerated and that companies should join the digital transformation. This is an obvious trend and nobody can afford to hesitate and waste time. Certainly, it is a challenge to build and integrate the necessary new capabilities, skills and technologies in the organisation and finance them until they start to produce additional value for the business, such as customer satisfaction, efficiency or new opportunities through new services.

I am really happy that E.ON, in terms of digitalisation, shows courage and determination to make investments in this way, to shape the energy world for customers and the company culture for our colleagues. We at E.ON Italia want to build a data-, insight- and emotion-driven company. Why? Because the world around us is developing in this way.

Customers are more and more connected; they have access to more information, their decisions are driven by rational (fact-based) as well as by emotional motives. And we want to use our human capital for more intellectual, creative tasks.”


Hyper-local intelligent content Created city- and district-based content for organic/non-paid web searches to explore new online customers’ acquisition channels by modelling the content relevance

Intelligent pricing B2B commodity competitors’ price monitor to enable intelligent pricing strategies for B2B small and medium- sized customers in Germany

Data-driven lead qualification for B2B solutions AI solutions for identifying prospects and qualifying leads for several campaigns using a semantic query engine and monitoring Internet news and other sources

Hyper-local customer segmentation PV Predicts the likelihood of conversion at lead level and establishes the optimal feature set to acquire new leads

Extended funnel solutions Exploits social listening and search tools to generate unique actionable insights for content strategy and search campaign management

Text analytics for customers’ and employees’ feedback Extract topics and opinion from non- structured texts providing topic modelling and in-depth NPS analysis using the newest deep-learning based techniques

B2B E-Mobility leads engine Automatically creates targeted, deal- winning offers based on a set of weighted factors leveraging openly accessible data and own information, providing also

insights for eMobility impact modeling for grid operators and roll-out planning intelligence for municipalities

Data-driven search boost Optimise organic and paid search in a joint manner to decrease the cost per acquisition in online channels leveraging

trends monitoring and ML based conversion optimization techniques

Fig. 8 overview of Data-driven Marketing Sales and services activities

E.ON is a customer-first company. Hence, one of the most critical areas for our business encompasses all activities along the customer journey for our B2C, B2B and B2M customers. We’ve organised these activities in different blocks, where advanced data algorithms can act in a very specific way on particular KPIs:

Some intelligent growth products highlights:

Growing our Business: Growth HackingThe more insights we generate about our customers, the better experience we can offer at each and every touchpoint. With data-driven content marketing, we can reach more customers even when they don’t have a clear need for any of the products or services we offer. In other words, we can massively extend the customers acquisition funnel. Analysing behaviours across online marketing channels allows us to tailor personalised experiences or even offers, so that we can be as relevant as possible with our ”customers-to-be”.

Establishing the right pricing points to optimise our conversions without damaging our customer lifetime value is a very challenging and attractive advanced-analytics problem. Only with the right data and right techniques, we are able to trace and monitor our competitors. Analysing social media feeds to understand how we are perceived vs. how they are perceived, or scanning the web for news related to energy, to uncover potential customers for any of our energy-efficiency solutions (be it a CHP, a Power Quality set-up, etc.), can give as a definitive competitive advantage. Likewise, optimisation of the marketing budget is increasingly relying on models that allow us to understand how a particular sales channel is assisting other channels, how every single euro can best be allocated, or how branding activities benefit sales.

The way we acquire customers beyond the pure commodity offering (gas and power), requires very strong decision-making capability applied to the marketing and sales leads along the sales life cycle. Qualifying the leads above a particular conversion likelihood threshold allows our sales force to focus on the right potential customers, instead of pursuing empty opportunities.

Chapter 8 – Growth Hacking & Customer related Analytics

82 83An overview of AI activities at E.ON Chapter 8 – Growth Hacking & Customer related Analytics

There are countless examples where data and AI are making a difference and creating massive growth opportunities. And it applies to the whole portfolio of products and services that we have, to all customers of all sizes (residential, B2B and municipalities) and to all our regional units. It’s really encouraging how different units have started to embrace the growth-hacking hiring even dedicated roles for this activity. As acquisition activities are typically governed by clearly defined KPIs, the impact of introducing algorithms can be fully quantified and is already reporting substantial benefits for all the adopters.

AI-based Customer-base ManagementE.ON serves 22 million customers in eight countries. Our customers deserve the best service, and as a company, we are committed to using the best technology and insights to provide it. We use the best machine learning approaches to better understand our customers, to learn their behavior and to detect situations where they are not completely satisfied with our offering and services. We systematically inspect chains of events and analyse their impact on the decision to move to a competitor. With the right data (events, competitive pressure, quality of service and a long list of further attributes) we can build models to quantify the proneness of a particular customer to leave us and we can make these models self-explanatory: we produce a human-readable list of arguments sustaining the output of the model, which makes our insights highly actionable, e.g. retention campaigns focused on addressing the most important drivers in a particular high-risk customer’s cluster. In addition, we want our customers to adopt many of our services, and for that we carefully analyse which products and services would be relevant for each particular existing customer and create cross-selling strategies, e.g. we can compute the breakeven for a commodity customer owning a house if a PV and battery solution was installed, and for customers where the business case is significant, we could run a campaign (always under observation of GDPR). Increasing the share of homes with own solar generation is certainly a win-win for our customers and our environment.

AI value creating cases along the Customer Journey

Customer Processes and Intelligent Automation

While the service quality to our customers cannot be compromised, it is important to reduce the cost to serve. In order to achieve this, we have developed a holistic intelligent automation framework, combining several technologies:

• Process Mining: data-inferred view of the entire process, identifying unnecessary steps, pinpointing bottlenecks, assisting in root cause analysis, identifying which steps can be automated and providing continuous monitoring to quantify improvements

• Robotics Process Automation: systems that mimic human interaction across various software interfaces (e.g. repetitively copying or moving data from tool to tool)

• Cognitive services: AI agents that deal with unstructured information that are required to feed and steer Robotic Process Automation (RPA) flows

(e.g. extracting a customer’s reference number from the body of one email to trigger an update at the back-end)

• Chatbots: conversational agent to replace human interactions in channels such as online chat, with enhanced capabilities to perform transactional tasks (e.g. triggering an update of a customer’s profile)

• Voice assistants: similar to intelligent bots, but providing voice-only interaction to conversational and transactional cases (e.g.: Alexa skills)


So far, we have seen several developments across the company in some of these areas. In the German sales organisation, the chatbot “Anna” has been successfully launched to assist our customers, as well as an Alexa skill 9 that allows for updating meter readings, answering bill-related questions or scheduling a callback from an agent. RPA scripts are running in multiple areas solving particular tasks, especially in the finance area and in network customer services. Bundling these technologies in an end-to-end framework allows for embedding more intelligence and creating increased value, as well as providing continuous monitoring of the gains.

Our commitment to our customers requires us to carefully listen to their feedback and concerns, to identify priority areas that we should definitively improve. Apart from the numeric Net Promoter Score (NPS) values, we can learn from our customers by processing all textual information generated at different touchpoints (call centres, surveys, feedback forms, etc.). With our text analysis technologies, we are in a position to infer a NPS score based on the sentiment of the textual information (we can quantify satisfaction even if we haven’t explicitly asked for a numeric value). In the same way, we can also extract topics (e.g. the bill) and subtopics (e.g. bill too high) automatically and monitor the importance of these topics and sub-topics over time (volume of interactions). On top of this information, we provide a driver analysis – what’s motivating a topic to have such a low NPS – thus generating immediately actionable insights.

Listening to our customers is essential and we are developing the right tools to become the best listeners.

Identify processes with highest improvement potential

Understand people, systems, interactions, dimensionality and complexity drivers

Data-driven analysis of the process to create full transparency

Run a holistic, e2e process analysis including data-driven problem identification, analysis and recommendation

Define a plan to tackle actions derived from process analysis, agree roadmap & implement

Define and monitor micro-KPIs Quantify value for every uplift

Identify Potential Descriptive Analysis Process Mining Process Analysis Action Planning Monitoring

Transparency Analysis Action

Voice Assistants

Chatbots

RPA

Cognitive Services

Process Mining

What does the process look like?

• Automated process mapping• Multi-system view• Automated updates

What are key improvement areas?

• Problem identification (Loop identification, time- elapsed analysis, perf. decay drivers)• Problem analysis (bottleneck & root-cause analysis, perform. break-downs, potential to apply RPA or introduce chatbots)

What changes to implement?

• Introduce bots & assistants• Automate via RPA• Prune process steps• Address root causes• Implement AI capabilities (NLP, NLU, MLL)

9 Available at https://www.amazon.de/E-ONGROUP-home/dp/B076T2MJSD


Fig. 9 intelligent Automation end-to-end framework

Anthony Ainsworth

CEO Solutions Management B2B

“In an increasingly competitive market for energy services data, but more importantly our ability to usedata, will be where the battle is won. We can be theenergy company that enables our customers to become more sustainable AND with great data-driven insights, demonstrate to those customers why it is good for their commercial bottom line.

I love the work of our Data.ON team! Starting with“What if?” and “Why?” their mission is to delivervalue-driven, actionable insights to the business andto our customers, to enable us to be faster, moreefficient and more effective – for B2B Solutions thatis in sales, operations and customer-facing platforms. I love the “growth hacking” project to deliver leads to the German sales team and I love the work done on Optimum to develop energy-efficiency algorithms so customers can quickly spot energy use anomalies and correct them.”


Extended Funnel Solutions

What?We created smart products through social media data, web news and several other sources, to capture and discover customers‘ web browsing patterns. Our algorithms, customised for each regional unit, automatically process multiple inputs to provide actionable insights, useful not only for E.ON’s overall content strategy, but also for paid and organic search optimisation.

Why?E.ON provides commodities and solutions which require more and more exposure to the public. We aim to establish E.ON’s presence on the web, reducing the digital marketing expense by using a data-driven approach. Through the identification of blind spots in our paid search campaigns and in our content coverage, we empower E.ON to appear at the top ranking on the search results pages and to be more easily found in order to widen the customer funnel. Smart usage of the available web data allows E.ON’s editors to be the first to create engaging content to capture the attention of potential customers and to increase customer engagement. At the same time, we provide E.ON’s digital marketing teams with tools for optimising their budget planning and the effectiveness of their marketing campaigns. The final result is to boost web traffic and conversions, reducing the overall cost per acquisition. Also, we increase E.ON’s share of voice, brand awareness and top-of-mind awareness in niche categories. These are places where no other energy player is putting its focus, yet they have huge potential.

Fig. 1 overview of the Trendspotter for the Italy‘s main campaigns

Our aim in the long run is to leave a positive mark on all the following metrics: 1. Lead conversion2. Avg. lead score3. Sales qualified leads4. Search traffic increase5. Returning visitors rate6. Returning visitors rate by sales qualified leads 7. Click through rate through funnel8. Time to conversion9. Cost per acquisition

How?We created and ran algorithms in our Azure cloud which continuously scan news and social media for trending topics, gathering a huge number of keywords which can be used for paid and organic search. For each new trending keyword we calculate the “closeness to business” or “closeness to campaign” index. Using Google AdWords, we monitor the search volume of these keywords and we gather information about our regional units’ digital campaigns. With our product “Trendspotter”, implemented in Tableau, we offer an easy approach for exploiting the precious insights for SEA and SEO optimisation. We constantly improve our product engine and front-end thanks to our stakeholders’ feedback and needs. Our MVP focuses on the Italian market in strict collaboration with E.ON Energia. Italy is our preferred target for Extended Funnel Solutions as 1) competitors are not as strong as in other countries 2) E.ON is the first in the digital energy field.

Result?We’ve assessed the potential of this technology both in traffic volume increase (between 5%–10%) and in traffic quality (the target is a minimum conversion rate improvement of 2.5%), based on the proof points we have. In addition, during the implementation, we generated a lot of assets that will ideally be included into the digital marketing operational business.

Hyper-local Intelligent Content Experience

What?Hyper-local content aims to create city- and district-based content for organic/non-paid web searches to raise consumer awareness of our products and services and increase conversions. Ultimately, it will reduce the average cost per acquisition (CPA) across all online channels and more importantly, bring E.ON and our sustainable solutions closer to our customers. In addition, this content is intended to support sustainable behavior and to help adopt a greener life-style.


Why?As we run our marketing activities to attract new customers, paid-search advertising is generally the channel of choice due to its almost immediate impact. However, it also comes at a high price whereas other channels such as organic search advertising are much cheaper and more durable. Our aim is turn locally-focused web searchers into prospects, which could not only facilitate conversions but also enhance cost savings due to a more competitive channel mix. Website visits, conversions and cost per acquisition are the main target measures. KPIs such as search visibility will also be tracked.

How?The project‘s MVP is a website with localised content for all Milan districts. Users will be able to compare 88 districts based on metrics such as energy efficiency and photovoltaic potential as well as less business-related metrics such as restaurant and transportation densities. In order to guide users to those districts that are the best fit to their lifestyle and needs, the website will also feature a recommendation engine. The information that users disclose will be tracked for the purpose of tailored calls to action and re-targeting.

Fig. 1 profiling of locations based on publicly available sources

Result?The project has been kicked off with an extensive local search and potential analysis. The resulting Local Search Dashboard provides an overview of topicsthat users search for locally and prioritises them on a city level. The necessary implementations for connecting the Google Ads API or distributing/masking Google Suggest queries are useful by-products. During the current MVP implementation, connectivity to our self-hosted OpenStreetMap server has been established and the first metrics have been defined and calculated.

Fig. 1 – customer facing interface

Hyper-local lead scoring for B2C solutions

What?Generating leads from our running online marketing campaigns or purchasing them from online portals can substantially rise the cost per acquisition. Having a model that predicts the likelihood of a lead to convert can help in decreasing this cost. By utilising customer data, data of solar panel installations and the outcome of lead interactions, we can build a machine learning model that is capable of estimating that likelihood.

Fig. 1 E.ON solar tool shading the roofs with usable surfaces for PV

Fig. 2 overview of leads distribution by channel in relation to the scoring threshold


Why?Our energy solutions, such as E.ON solar or E.ON drive, require an upfront investment larger than the typical commodity contract. Many potential customers manifest their interest, but not all of them have the same purchasing intention. A scoring model allows us to sort leads by their quality. Consequently, we can focus on high-quality leads and discard the lower-quality ones. The process of customer segmentation is based on historical customer data. This data is collected from CRM systems, the solar calculator and aggregators. The collected data is pre-processed, for example, to fix inconsistencies and discard duplicates, and then used to train a classification model. More specifically, based on positive (won) and negative (lost) samples (leads) in the training data, the classification model learns to predict the conversion probability of a new lead. The main KPIs are the CPO (cost per order) and the conversion rate. However, since we may discard customers, we also have to monitor, for example, total conversions per month. Typically, we would accept, at most, a small decrease here but only if the other two measures overcompensate for this effect.

How?There are two main deliverables: 1) a scoring model for leads provided by aggregators; 2) further deliverables need to be clarified (potential use cases: opportunity scoring, scoring of all the leads, estimating the impact of responsetime from lead generation to first call).

Result?We have built a model to predict end-to-end lead conversion based on data from Germany , Sweden and we have started applying the same concept to qualify the boilers leads in Romania. A ”live back-test” was performed and the results were presented to the stakeholders and domain lead. We are now generalising the model and extending it to further types of products -boilers, EV, etc-, but also to different states in the customer journey, for example, quotes scoring. In this context, several use cases (e.g. lead qualification, conversion prediction, customer segmentation) have been further identified to grow the lead engine.


Digital Control Center

What?We are building up a digital marketing data platform for all seven regional units with internal and competitor traffic data for over 60 E.ON domains and mobile apps. This platform functions as an enabler for B2C and B2B Growth Hacking. The platform is being built up step by step, starting with B2C commodity and B2C solutions in various regional units, then expanding to include more segments and the remaining regional units.

Why?This project follows three main goals. Firstly, to increase traceability – the goal is to track the progress of our overall digital marketing activities. Secondly, to standardise performance measurement – the aim is to have a consistent way of measuring our digital marketing performance and implement best practices across regional units. Thirdly, to enable growth hacking – collecting a common and normalised set of data is critical to assess the potential and final impact of all growth hacking activities. Becoming a KPI driven company allows for a better embedding of AI capabilities as well as continuous impact traceability. KPIs are enhanced with predictive models for projections but also for realistic targets setting.

How?The Digital Control Center Platform is built up as a PaaS (Platform as a Service) on the Microsoft Azure Technology Stack. To collect the data, five online data sources are used (GA, GA BigQuery, SearchMetrics, Similar Web and AdWords). In addition, various CSV files (e.g. sales targets, marketing spending) from different E.ON back-end systems (e.g. SAP/CRM) are integrated to calculate CTS and CPO. Finally, the front-end visualisation is done with Microsoft Power BI.

Fig. 1 digital commercial acquisition activity overview

Result?The Digital Control Center is live and collects available and approved data for all our regional units and segments, offering a balanced scorecard view, a deep-dive analytical view and an open architecture to incorporate each and any kind of business metric. With the current solution, E.ON‘s sales and services performance can be monitored through a comprehensive management dashboard. Moreover, business analysts can build their own dashboards using the analysis service. Lastly, data professionals can download and use raw data directly from the data lake directory for various analyses.


Data-driven Search Boost

What?Web analytics and digital marketing tools are a fundamental piece for E.ON’s online marketing strategy. Applying the right AI techniques in this space allows us to create value by significantly increasing traffic on the E.ON website without increasing the digital marketing spend. We aim to optimise organic and paid searches and to enable automated bidding opportunities for E.ON’s digital marketers.

Why?E.ON’s expansion in the European market relies to certain extent on the company’s success on the web. A more effective and cost-efficient SEA strategy is crucial for running digital campaigns and increasing ROI. The smart, fine-tuned optimisation of digital marketing and of the SEA/SEO balance will boost performance without additional investment. For this, a complete automated analysis of aggregators’ and competitors’ web performance, as well as the monitoring of search engine trends, is necessary. With our complete and accurate analyses, we manage to leverage E.ON in search engines via automatic competitor and keyword analysis. Then we want to take it to the next level with our machine learning algorithms, which will enable us to develop a cost- and result-efficient SEO and SEA, bringing automated bidding into E.ON. Our aim in the long run is to leave a positive mark on all of the following metrics: 1) lead conversion, 2) average lead score, 3) sales qualified leads, 4) search traffic increase, 5) returning visitors rate, 6) returning visitors rate by sales qualified leads, 7) click through rate through funnel, 8) time to conversion, 9) cost per acquisition.

How?As a first step, we provide our stakeholders with suggestions of best practice for a more efficient and effective SEA strategy, leveraging all kinds of data provided by digital marketing tools (Google AdWords, SimilarWeb, Searchmetrics, etc.). A complete SEA, SEO and traffic audit enables the calibration of the SEA/SEO efforts in order to cut their costs and boost performance. Cross-matching Google AdWords data with SEO generates a new and more complete way of reporting and facilitates strategic decision-making, with the aim of increasing traffic and conversion rates. Finally, through technical optimisation, we open up new business opportunities to attract more high-quality traffic and create geolocated marketing campaigns.


Result?The Data-driven Search Boosting MVP is completed in E.ON’s German market, where the brand is well established on the web. We aim to maximise conversions and ROI and to optimise the E.ON advertising spend using data analysis and machine learning. We are creating several assets, such as automated data-based keyword analysis, SEA automatic bidding, hyper-local search trends and bot/crawler detection.

Fig. 1 detailed geo-located keyword analysis for commodity in Italy

Data-driven Lead Qualification for B2B solution customers

What?The aim of Data Driven Lead Qualification is to develop AI solutions for identifying qualified leads for B2B campaigns and use the synergies between the different campaigns. In addition, the intelligence data and algorithmic assets, as well as the extracted knowledge about solutions, competition, customers/prospects, regulations framework, etc is designed to increase the efficiency of our B2B sales campaigns as we run them.

It allows us to identify a set of potential customers from a large set of internal and, as well as providing the right insights on why/to which degree a prospect qualifies for which solution. The generated leads can be directly used by the key account managers and are already classified for different channels, e.g. phone, LinkedIn, Xing, etc.

Why?Intelligent techniques put us in the position of anticipating customers needs very early in the customer journey and approaching them with the right offer for them. Optimizing relevance is therefore one of the most critical tasks we accomplish. In addition, it helps to save time for the customer as well as for sales agents (improved resource allocation), while maximising the capabilities of our sales force, as we put them in touch withthe right customer at the appropriate time.


How?The Data Driven Lead Qualification project uses various AI products which are available as a standalone service or as a products to be integrated in an end-to-end pipeline. The advantage of end-to-end integration is that the feedback of the experts and customers can directly be integrated into the process of lead quality assessment to improve the quality of the leads after each step. The user, e.g. campaign manager, can use one or more of our AI solutions combined and get a set of relevant leads for their specific campaign.

Result?We designed and developed an end-to-end process for targeting leads for five different campaigns in Germany. In this process, we map leads to campaigns and automatically identify the channel for targeting the leads, e.g. phone or LinkedIn Sales Navigator. For example, the power outage monitor is one of these products, where we can automatically track the occurence and impact of power outages and quality decay at a very localized level and identify the set of potential customers which strong power supply quality needs. It has been successfully tested in Germany and we are now exploring the roll-out to other countries.

Fig. 1 power Outage Monitor

Fig. 2 postcode sector shaded by number of outages


E-Mobility Intelligence Suite

What?E.ON is taking a leading role in the adoption of electric mobility from 2 perspectives: on one hand, as distribution network operator, extending and adjusting the capacity of the grid to support new peak situations created by new energy needs, on the other hand, as active commercialization entity, taking to market E-Mobility solutions for our B2B and B2C customers. Hence, we created an intelligence engine which, while preserving the unbundling regulations, can leverage machine learning techniques in both scenarios. In the B2B Leads Qualification Engine Project, we are drawing from the abundance of data from both external and internal sources to predict which B2B customers are more likely to need, and benefit from, charging solutions – in other words, to see how well they can be electrified! Our goal is to qualify B2B leads for E-Mobility, to create targeted, deal-winning offers and to boost our Sales teams’ successes, while providing our customers with benefit solutions from both economic and environmental perspectives. We have started with more mature E-Mobility markets (Nordics), and are bringing the corresponding learnings to less mature markets. Further modules provide commercial insights for municipalities, to drive the adoption of E-Mobility and start campaigning for the adoption of clean mobility. For example, our algorithms can divide any area in Europe in hexagons with different resolution and predict the adoption and the demand for E-Mobility. Likewise, we can predict the total number of users per charging point per time unit (2h, 4h, 1day, etc). In addition, this is our first data incubation project, where we pursue a bottom-up approach to value creation starting with data and creating insights until we get a proof of value.


Why?With eligibility scoring, potential B2B E-Mobility customers can be approached with the most relevant offering, not only with a justified and explainable scoring for each and every location of theirs, but also a full-fledged business case and detailed demand projection over time. The goal is to increase sales conversions and to open up time for the sales teams to improve their go-to-market activities, while giving our customers certainty on when their investment is going to pay off and monitoring their contribution to the energy transition. Our grid operations team can equally injest these insights into the grid planning tools to be ready to meet the E-Mobility related increasing demand.

How?Using publicly available data from OpenStreetMap (OSM), census, charging infrastructure, POIs type and density, car registration data, etc) we are able to create a scoring system for arbitrary locations, e.g. supermarkets, hotels, department scores. Relevant features for scoring are for example, the number of charging stations in the vicinity, the distance to points of interest and the electric car density in the area. We have created a web-based dashboard using HTML5 technologies, called ”E-Mobility Leads Engine“ to view and analyse the results. For further information please see the E-Mobility Leads Engine Data.ON blog entry for the Nordics MVP launch.

Fig. 1 overview of the engine

AI assisted Customer Base Management

What?We have created state-of-the-art machine learning models to detect/predict situations all along the customer journey on one hand and to engineer the proper communication/offering to address these situations on the other hand. For example, in countries like Germany, when customers receive their energy bill once a year, the amount on the bill might be unexpected -smart meters are just starting to been rolled out and consumption variations are not transparent to the user-. The so-called “bill-shock” can be modelled but also the impact at customer level for those being affected. With these insights, targeted campaigns can be put in place to explain the unexpected bill but also to recommend more suitable plans given the changes in the consumption. Targeted models have been created for each and every relevant situational change along the customer lifetime (e.g. home move, price increase due to the variation of the grid fees, end-of-contract, etc), but also specific to acquisition channels (e.g. to detect contract-hoppers) and to model the influence and exposure of the competing offers. In addition, we also created a generic propensity model updating the scores at customer level on a regular basis. Each and every model is explainable so that it can be addressed by particular campaigns.

Why?E.ON is a customers first company. Understanding our customers is key for us to address and anticipate their needs and stay relevant with our products and services. Unlike other industries, the number of touchpoints an average customer has along her/his lifetime with us is rather low. The limited availability of data introduces a challenge for us to gain enough information about our customers and makes even more critical the usage of machine learning models to predict customer behavior.

Result?MVP has been successfully launched in the Nordics: a dashboard is available to score and qualify 260k amenities belonging to 6k leads (in the Nordics and Germany), and create a first model for predicting the consumption at a given new potential location.

Fig. 1 Customer Base Management Algorithms at a glance



How?Our models are created using curated historical data encompassing transactional registers, socio-demographic data (always under preservation of GDPR), contextual data (micro-markets, etc), marketing and offering data, etc. For the modeling we use several techniques, ranging from the well-known xgboost to more sophisticated deep learning-based methods. We use the well-known LIME and SHAP explainers. The whole pipeline is engineered in our cloud complying with our coding best practices and with the engineering and security standards. As we are using and adapting all components with different data in several markets, the pipeline is designed to be modular and easily to adapt.

Result?We have several models LIVE in 3 countries and the roll-out continues. In Italy we coupled the generic model to a retention campaign that has been extremely well received by our customers (major improvement of several percentage points in the retention rate). In Germany, more targeted models have been deployed and are being tested in different campaigns with encouraging results as well. In the case of Sweden, the generic model is already providing results and different campaigns are being engineered using the explanaibility insights.

Fig. 2 details per category

Text Analytics for Customer Feedback

What?The goal of this project is to apply state-of-the-art text mining techniques to content generated by E.ON customers across different countries, automatically extracting the topics, opinions and NPS-related information.

Why?Opinion and topic mining enables E.ON to automatically extract the feedback from customers, and it provides the micro-tracking of NPS without having to rely on costly surveys. Our results have shown that NPS is highly correlated to the customer sentiment that we automatically extract from the comments. This enables us to use


How?In order to automatically extract topic and sentiment information, we have built models combining both open source and cloud cognitive functions able to extract entities, topics and sentiments, as well as clustering the textual content within different categories. For visualising the results, we are using Tableau (Figure 1) as well as Kibana (Figure 2).

Result?We have built high-performing algorithms and integrated them within an interface, which has been implemented for the UK and Italy and adapted for the overall roll-out. The next steps will include implementing a data pipeline to guarantee a continuous data supply and adapt the methods to work with German, Italian, Romanian, Hungarian and Czech data.

Fig. 1 topic and Sentiment Discovery Tableau interface. We label high-level topics by leading terms. The size of each rectangle corresponds to the frequency and the colour indicates

the predominant sentiment for each extracted topic. Hovering on a term displays all related subtopics, with the associated sentiment as well.

Fig. 2 Kibana interface for the Topic Sentiment Search Engine

the sentiment (which is free and immediate) to approximate the NPS score for each customer interaction. Hierarchical topic modelling is essential to understanding our customers and how topics relate to each other.


Chapter 9 – outlook

After the efforts from the last 2 years, where we’ve made a breakthrough, we are now striving for maturity. We have countless initiatives where we have proven that data and AI can create value and we have quantified this value. We have launched a powerful transformation programme that is already delivering tangible results and we’ve extensively proven AI’s potential to reshape our business. We can proudly state that we have crossed “the AI chasm”. In the upcoming years, we need to transition all our projects and activities to become mainstream.

AI delivery maturity

We can only reach the AI mainstream state if we streamline our delivery, exploit all potential synergies, roll out our intelligence products to a wider proportion of businesses and manage to embed our AI systems into the line operations of the company. Hence, we need to define clear focus areas:

Scaled and industrialised AIWe have already started componentising many of the assets we have across projects: algorithms, data preparation routines, data sources, visualisations, etc. We have standardised some of the documentation processes (e.g. machine learning canvases, etc.) to have a common understanding of the assets available across projects. We have created data services to expose curated sources of information (e.g. weather data, market economics data, etc.). The basis for scaling up our delivery is having a solid componentised repository of data and algorithms and a proper knowledge base. A new AI project can then be delivered by orchestrating existing components. The development of our data engineering capabilities is a must to support the proper industrialisation.

“In 2018 we started our digital transformation by building our digital team; educating our colleagues about the value and opportunities of digitalisation; designing and implementing our data management technology; exploring, testing and learning in digital marketing, sales and some operational processes. In 2019 and 2020, we will scale up the programme to reach the full potential of the digital technology and competencies as quickly as possible. Transforming the legacy in parallel with building a challenger company and culture – this is an exciting mission for everybody in our company!”

Peter Ilyés

CEO E.ON Italy


Widespread AI adoption

We have proof of value generation in many projects. This means our deliverables have been back-tested and are already used to take business decisions, and the impact of these business decisions has been quantified. Let’s take, for example, “intelligent lead qualification” for the solar and battery business in Germany. Lead qualification for PV is required in many other countries, not only in Germany. In addition, other solution domains (e.g. heating, E-Mobility) can benefit from the same capability. In order to scale up, we are now focusing on the roll-out of the value-proven data products, both geographically and by solution domain. The same holds true across B2C, B2B, B2M, and of course our energy networks.

Embedded AIIn some cases, the products we have created and taken live are used on a regular basis, yet not fully integrated into the operational business. We need to make these data and AI products a non-disputable, integral part of the day-to-day decision making process, which requires the even tighter integration with the current IT landscape. We will push for embedded AI systems to make sure the adoption takes place as seamlessly as possible.

Blockbusters or needle-moving projects A blockbuster can be defined as a high-impact, high-visibility global project with a short delivery time, attracting a high amount of management attention. In the end, it is about creating a big impact, in terms of fundamentally improving a company-wide KPI. We are in constant alignment with our strategy teams to identify potential blockbusters.

Transformation

We kicked off the transformation programme and have managed to deliver concrete and tangible results starting in 2018. In 2019, the transformation continues to penetrate to penetrate the whole organisation, so we are working on extending the reach but also on quantifying the results more intensively. At the same time, we are introducing a standard to track all our data capabilities and skills against, so that we can sharpen our Dat-A-Cademy offering. To further drive our transformation, we will put a stronger focus on the following topics:

Data management and governanceAfter all the preparatory work delivered so far, our data-readiness initiative is ready for a large-scale roll-out. The year 2020 is where data will become a first-class citizen across E.ON. Starting with low-complexity countries, we will roll out our Meta Data Management tool, deploy the right roles and implement the proper governance processes. We will see benefits from the very beginning and will be monitoring the impact very closely.

We are amongst the first large companies to embark on such a data-readiness quest and we are aware of the first-mover challenges, but there is no easy route if we are serious about creating a real and sustainable impact with data.

AI cultureWe will intensify our evangelisation activities to get the organisation hands-on with data. By the end of 2020 every single employee should be aware of how data is taking E.ON forward on a global scale, but also how data can boost the performance of his or her particular area. In addition, our Dat-A-Cademy will also continue to scale and reach more employees at every level of the organisation, from top managers to junior employees. We are standardising the profiles and skills we have available at the organisation and we are developing an intelligent system to identify expertise and connect experts with the community in specific areas.

We will continue enabling subject matter expert-moderated communities to connect all data professionals beyond departmental borders. To the existing Data.ON hubs, we will add further countries and business units to fully exploit all synergies and leverage our global-local model.

Enabling new technologies and partnershipsWe want to professionalise key technologies that have been partially implemented in a fragmented and isolated way across the organisation. In particular, intelligence automation – where we want to deploy an end-to-end automation framework, in which we will bundle process mining, robotics process automation, chatbots and voice assistant technologies and cognitive services alongside orchestration capabilities. Both for scaling our existing capabilities and explore new fields, companies need to establish the proper partnerships in the Academia and the Industry (with big players, specialized boutiques and start-ups). We are already partnering with leading universities and tech giants, but we will expand the collaboration to new partners to address for example the AI/Quantum computing research space.

Innovation

We are planning to intensify our bottom-up innovation to land high-value disruptive ideas. In order to do that, we will join forces with other innovation initiatives and teams, whenever meaningful, whilst also preserving our autonomy to retain the ability to start piloting ideas from scratch.

We will explore inter-company agreements (data alliances, data sharing agreements, etc.) to leverage data from several industries together and boost innovative ideas. We are also planning to increase the exposure of our output to customers and to set-up co-development spaces. In 2019 we have already proven, that we can create disruptive products up to bringing them to live environments. By 2020, we expect to generate external revenue and start exploring different external AI and data monetization strategies.

Chapter 9 – outlook


Matthew Timms Chief Digital and Technology Officer

of E.ON SE Matthew joined E.ON SE as the Chief Digital Officer in August 2016, leading the digitaltransformation organisation and programme for E.ON globally.

He has had over 20 years experience of building innovative digital businesses and capabilities across a number of industries, including software, automotive, pharmaceuticals and financial services. With a strong digital and business leadership background, he has experience in building high-growth digital businesses within large corporations.

Previously, he was responsible for building the digital transformation capabilities at SAP. Before this, was responsible for commercial leadership of digital business units in financial services for over 8 years, with Santander and the Lloyds Banking Group.

Matthew attended the University of Southampton, where he completed an honours degree in physics. He enjoys spending time with his family of three children and in his spare times he likes to get outdoors and about – either running, hiking, sailing or skiing.

Juan Bernabé Moreno Chief Data Officer

of E.ON SE Juan received M.Sc. and Ph.D. degrees in Computer Science from the University of Granada, Spain, in 2002 and 2015, respectively.

He has been leading data science teams in the telecommunication industry (product data lab for Telefónica Digital and the web intelligence team for Telefónica Germany) for more than 8 years and at present is the global head of Data and AI at E.ON SE. His current research interests include fuzzy linguistic modelling, the aggregation of information, information retrieval, bibliometrics, digital libraries, web quality evaluation, recommender systems and social media.

Some of his work has been recognised as ”best paper“ at different conferences.

Juan is a renowned data science evangelist specialised in exploiting statistical learning techniques to optimise business results in large corporations. He continues to be involved in research activities with the clear aim of closing the gap between academia and industry.

About the authors

Karsten Wildberger Member of the Board of Management

of E.ON SE

Karsten studied physics at the Technical University of Munich and RWTH Aachen University. After his scientific research at the Jülich Research Centre (Forschungszentrum Jülich), he obtained a PhD in theoretical physics from RWTH Aachen University. Karsten also holds an MBA from INSEAD in Fontainebleau, France.

Karsten worked as a management consultant at The Boston Consulting Group in Düsseldorf from 1998 to 2003. In 2003, he joined Deutsche Telekom, where he served in leadership positions in London and Bonn until 2006.

From 2006 to 2011, Karsten worked in various senior executive roles in Finance and in Business at Vodafone plc, such as Chief Financial Officer and Chief Commercial Officer in the Management Board of Vodafone Romania.

After having returned to The Boston Consulting Group as a Partner and Managing Director, Karsten was appointed Group Managing Director of the Consumer business and the Digital Transformation of the Australian telecommunicat-ions group Telstra in Australia. He subsequently was also responsible for Telstra Products and for Telstra Business as Group Executive Telstra Retail.

Karsten sat on the Board of Directors of the Telstra Foundation and the Telstra Ventures Group. Since April 2016 Karsten Wildberger has been a member of the Board of Management of E.ON SE. He is responsible for E.ON’s Retail and Customer Solutions Business, Decentralized Generation, Energy Management, Marketing, Digital transformation and IT.

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