Benefits of big data analytics in Smart Metering, ADEPT, WICKED and beyond

11

Benefits of big data analytics in Smart Metering, ADEPT, WICKED and beyondProf David Wallom

Associate Professor and Associate Director - Innovation

2

Advanced Dynamic Energy Pricing and Tariffs (ADEPT)

• Objectives– Understand the limitations that domestic consumers are willing to consider acceptable in terms of

dynamic pricing tariffs

– Investigate the relationship between dynamic electricity tariffs and power network characteristics,

– Design a scalable computational and data platform

• Turning data into actionable information– Exploiting well known & developing innovative data mining techniques, predicting and classifying costs, determining

behaviour type and response to tariff changes and other inputs

3

Clustering domestic consumption using Dirichlet Process Mixture Model

• EC FP7 Dehams dataset (www.dehams.eu, UK & Bulgaria)• Using a Bayesian method allows us to handle uncertainty within the data

set more easily than more traditional data mining methods• Clustering defined by data not user

6

How complex can and should a dynamic energy tariff be?

Normalised daily power demand profiles for all businesses by sector

An illustration of the differences between the tariffs used and the typical variation of the RT

Commercial consumption data thanks to OPUS Energy

7

When should you change tariffs?

8

Which % are winners?

9

Can we predict winners?

• Binary Classification problem Winners (RC <1) & losers (RC>1)• Three body classification problem Winners (RC<1-e), Neutral (RC<> [1-e, 1+e]) & Losers

(RC>1+e)• Classification using Machine Learning; Artificial Neural Networks (ANN), Support Vector

Machines (SVM) and Naïve Bayes Classifier (NBC); FPT-RTT TOUT - RTT

W ICK ED

http://www.energy.ox.ac.uk/wicked/

Infrastructure Technical

OrganisationalLegal

Creationof

KnowledgeEnergystrategy

Development

Working with…

Oxford Departments• Maths• OeRC• ECI• Law• Eng Sci

W ICK ED

http://www.energy.ox.ac.uk/wicked/

MISSING SLIDE

May 15, 2015

W ICK ED

http://www.energy.ox.ac.uk/wicked/

May 15, 2015

W ICK ED

http://www.energy.ox.ac.uk/wicked/

May 15, 2015

W ICK ED

http://www.energy.ox.ac.uk/wicked/

What effects do large energy efficiency projects have?

W ICK ED

http://www.energy.ox.ac.uk/wicked/

What effects do large energy efficiency projects have?

W ICK ED

http://www.energy.ox.ac.uk/wicked/

What contributions to my overall energy consumption do different store types make?

W ICK ED

http://www.energy.ox.ac.uk/wicked/

What contributions to my overall energy consumption do different store types make?

W ICK ED

http://www.energy.ox.ac.uk/wicked/

Which of my property portfolio should I concentrate investment on?

W ICK ED

http://www.energy.ox.ac.uk/wicked/

Which of my property portfolio should I concentrate investment on?

W ICK ED

http://www.energy.ox.ac.uk/wicked/

Where should I be looking examples of best practice?

W ICK ED

http://www.energy.ox.ac.uk/wicked/

Where should I be looking examples of best practice?

W ICK ED

http://www.energy.ox.ac.uk/wicked/

Modelling the system for greater understanding

W ICK ED

http://www.energy.ox.ac.uk/wicked/

24

Clustering domestic consumption using Dirichlet Process Mixture Model

• Using a Bayesian method allows us to handle uncertainty within the data set more easily than more traditional data mining methods

• Clustering defined by data not user

25

DIET – Data Insights against Energy Theft

• ~£400M in theft per year• £8 - £20 per property per year• Key Smart Metering commercial

driver of reduction in human interaction.

• 2 year Innovate UK• British Gas(Lead), G4S & EDMI

• 300k meters per day, commercial customers

• 48 half-hour kWh readings per day• Training through confirmed theft

events

• How to scale to near real-time for 50M meters?

• ~50k potential theft triggers per day

• To use consumption and event data to identify energy theft• Evaluate new methods outside of TRAS with view to

inject new ideas into the next TRAS review • Investigate methods specifically to address smart

meters which can be facing different kind of challenges

26

Conclusion

• Utilising analytics to gain understanding of drivers of energy consumption within domestic and commercial customers requires;

– High quality data (low failure rate, we have seen the opposite)– Detailed metadata available

• We are able to link business and domestic consumer behaviour to energy consumption

– Meaningful questions to answer!

• Need to create new algorithms to cater for different and hitherto not well utilised data sources.

– Link consumption and non-consumption time series data to provide analytic triggers for a new use case which causes smart meter anxiety

27

Thank youQuestions?

With thanks to;• Ramon Granell, Sarah Darby, Katy Janda, Russell Layberry, Peter

Grindrod, Malcolm Muculloch & Sue Bright• Colin Axon, Ioana Pisica & Gary Taylor• Opus Energy, M&S, Dixons Carphone, & British Gas

28

Publications

• Granell, Ramon; Axon, Colin; Janda, Kathryn B.; Wallom, David (2016): Does the London urban heat island affect electricity consumption of small supermarkets?. figshare. https://dx.doi.org/10.6084/m9.figshare.3423130.v1

• Granell, R., Axon, C.J., Wallom, D.C.H. et al. “Power-use profile analysis of non-domestic consumers for electricity tariff switching”, Energy Efficiency (2016) 9: 825. https://dx.doi.org/10.1007/s12053-015-9404-9

• Granell, Ramon; Axon, Colin; Wallom, David (2016): Which British SMEs might benefit from electricity dynamic tariffs?. figshare. https://dx.doi.org/10.6084/m9.figshare.3423139.v1

• R. Granell, C. J. Axon and D. C. H. Wallom, "Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles," in IEEE Transactions on Power Systems, vol. 30, no. 6, pp. 3217-3224, Nov. 2015. https://dx.doi.org/10.1109/TPWRS.2014.2377213

• R. Granell, C.J. Axon, D.C. H Wallom, “Clustering disaggregated load profiles using a Dirichlet process mixture model”, Energy Convers Manag, 92 (2015), pp. 507–516

• Wallom, David; Granell, Ramon; Axon, Colin (2015): Feature extraction to characterise and cluster the energy demand of UK retail premises. figshare., https://dx.doi.org/10.6084/m9.figshare.1541107.v1

• Granell, R.; Axon, C.J.; Wallom, D.C.H. Predicting winning and losing businesses when changing electricity tariffs. Appl. Energy 2014, 133, 298–307.

• C. J. Axon et al., "Towards an understanding of dynamic energy pricing and tariffs," 2012 47th International Universities Power Engineering Conference (UPEC), London, 2012, pp. 1-5. https://dx.doi.org/10.1109/UPEC.2012.6398452