e-Research & the art of linking Astrophysics to Deforestation

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e-Research & the Art of Linking Astrophysics & Deforestation, via Smartening Energy Systems and Detecting Energy TheftProf David Wallom

Associate Professor and Associate Director - Innovation

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Overview

• e-Research Centre?

• The Astrophysics starting point

• Smartening Energy Systems

• Understanding drivers of energy consumption

• Energy Theft

• Deforestation and the supply chain

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Cambridge

Newcastle

Edinburgh

Oxford

Glasgow

Manchester

Cardiff

SouthamptonLondon

Belfast DL

RAL Hinxton

UK e-Science CentresOxford e-Science Centre• Distributed virtual research centre• Multiple Domain scientist, Computer Science and IT

services

Oxford Interdisciplinary e-Research Centre• Physical research centre with Vice Chancellor

support for start-up• Sited outside university structure to be truly

interdisciplinary

Oxford e-Research Centre• Physical research centre with new building• Part of the regular university• Research only – no student admissions i.e. teaching

Image courtesy Tony Hey

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Innovative digital methods transforming research

A collaborative research hub for

Digital OxfordA full department in a leading science division

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The ‘OeRC Process’

Research Problem

Application modelled

Create or adapt

technology

Disseminate and reuse

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e-infrastructure Energy and Environmental ICT

Smart Metering

Ecosystem Services

Energy Services

Volunteer Computing Citizen

Science

My Research Domains

Cloud/e-Infra Policy & Education

Cloud Technology

Cyber-security

Active Data Management

IoT

HPC/Big Data Infrastructure

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How we connect Astro-Physics and deforestation…?

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Update on SKA Status

David Wallom

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Pelican is a C++ framework for parallel quasi-real time data processing.

Framework for processing streaming data.

Framework handles the data flow allowing users to concentrate on algorithm development.

Modularity allows reuse of components between implementations.

Used for processing radio astronomical data in quasi real-time.

Deployed on LOFAR interferometer stations providing pre-processing for pulsar searching.

Has been used on real hardware across Europe since autumn 2010.

The Pelican Pipeline Framework

With thanks to Stef Salvini, Ben Mort and Fred Dulwich

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Links

Provide uniform API for application designers

Provide uniform function to handle I/O

Provide controlled interaction with scheduling system

Astro Physics

HPC Application Hosting Platform

High Performance Computing Technologies for Smart Distribution

Network Operation

Duration: 1st February 2010 - 31st January 2013Funding: EC FP7Countries: Slovenia, Germany, Israel, Spain, France, UK

Goal:• develop a new generation of distribution network

management systems that exploit novel near to real-time HPC solutions and intelligent communication

• support integration of self built, external provider and COTS products applications in this single environment

Motivation

Traditional Systems Future Smart-Grids

Application wrapping

Non-Pelicanapplication

HPC Engine

Pelican Server

Pipe line

Pipe line

Parallel Pipeline

Pelican Server

Scheduler

control &metadata notifications

HPC

-DS

Interface

DMS

results

data

HPC Engine and Storage

Next Generation Infrastructure

The Smart Grid

High Speed Communications System

Service Restoration

Voltage Control

Condition Monitoring/

Data Mining

DistributionSystem State

Estimation

Distribution Management System

Smarter Distribution

Distribution System State Estimation

Service Restoration Algorithms

Condition Monitoring

Voltage Control

Distributed State Estimation – Disjoint zones Algorithm

Implementation: PELICAN Algorithm

Compute nodes

3700 nodes(15 zones)


1 103 [s] 7.8 [s]

2 93 [s] 6.3 [s]

3 88 [s] 3.9 [s]

4 88 [s] 3.6 [s]

Compute cores


1 1139 [s]

4 294 [s]

16 88 [s]

French – rural network Virtual network

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Links

Astro Physics

HPC Application Platform Energy Distributio

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Computation & Data aaS

• Development of ‘Computation aaS’• Computational services to support data

mining and machine learning for condition monitoring

• New methods to handle uncertain data

Advanced Dynamic Energy Pricing and Tariffs (ADEPT)

• 3 year project, started 1st October 2010• University of Oxford: Oxford e-Research Centre, Environmental Change Institute• Brunel University: Brunel Institute of Power Systems

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

tariffs for electricity through consumer and consumer data interactions,

– Investigate the relationship between dynamic electricity tariffs and power network characteristics using an agent-based model to represent a segment of distribution network and its consumers,

– Design a scalable computational and data platform that run data mining applications to identify features of network and consumer behaviour and their relationship to input factors such as price and power demand.

Dataflows and Computational Services

Creating Actionable Information

• Exploiting data mining techniques:– Predicting and classifying costs when there is a shift in the type of

tariff, e.g. shifting to a real-time tariff from a fixed price tariff.– Clustering of domestic load profiles, determining behaviour type and

response by the consumer to tariff changes

• Utilise the EC FP7 Dehams dataset (www.dehams.eu, UK & Bulgaria) to provide domestic load data

• Utilise both well known (k-means) & innovative clustering techniques

– Dirichlet Process Mixture Model, a Bayesian non-parametric statistical clustering model

• investigation utilising data from commercial energy aggregation companies to quantify benefit per commercial sector of the transition to real time energy pricing

http://www.dehams.eu/

Clustering 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 Performance

• First Bayesian non-parametric model to cluster electricity load profiles

• Results are similar than other clustering algorithms but number of clusters is not a user input parameter

Real time pricing

• Analyse the impact of introduction of time-of-use and real-time pricing strategies

• Dataset from >12k UK businesses • 5 major sectors with 44 sub sectors

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

Temporal resolution

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Astro Physics


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Machine learning and analytics

Energy System Data


Links

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

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

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MISSING SLIDE

May 15, 2015

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May 15, 2015

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May 15, 2015

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What effects do large energy efficiency projects have?

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What effects do large energy efficiency projects have?

W ICK ED


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

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

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Which of my property portfolio should I concentrate investment on?

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Which of my property portfolio should I concentrate investment on?

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Where should I be looking examples of best practice?

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Where should I be looking examples of best practice?

W ICK ED


May 15, 2015

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Links

Astro Physics


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Smart Metering

Data Mashing

Machine Learning and Analytics

Energy System Data


• Linking consumption and non-consumption time series data to provide analytic triggers

Investigating domestic load profiles

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DIET – Data Insights against Energy Theft

• ~£400M in theft per year• £8 - £20 per property per year• Smart Metering only commercially

viable by reducing human interaction.

• Data Insights against Energy Theft (DIET)

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

• 300k meters per day, commercial customers

• 48 half-hour kWh readings per day• Details of 200 confirmed theft events

provided by partners ‘on demand’

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

• ~50k potential theft triggers per day

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Links

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

Astro Physics


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Smart Metering

Data Mashing

Energy Theft Detection

Machine learning and Analytics

Energy System Data


New model development

INFORMInternational Forest Risk Model

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Wood Pulp & Paper Palm Oil Beef Soy Leather

Risk

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Forest risk commodities expose supply chain participants to business risk

Reputational

OperationalLegislative

Deforestation Ratio

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Production Point of AggregationProduction Point of Aggregation

Production Point of Aggregation

Production Point of AggregationWeighted

Sum

Point of Aggregation

Deforestation Ratio

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Production Point of AggregationWeighted

Sum

Point of Aggregation


Weighted Sum

Processor/Consumer

How can a Risk Model be used?

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Municipalities

Export Port

Import Port

Processor

Customer

Farmer Le ACME Animal Feed

Sète

Paranaguá

Cocalinho

Novo São Joaquim

Rondonópolis

…

Santos

Araguaiana

Barra do Garças

Itiquira

…

São Francisco Do Sul …

Non tropical Countries ---

I buy my animal feed from Le ACME

Animal Feed in the South of

France

Supermarche wants to know

if my pork contribute to

deforestation?

Rondônia - Deforestation Trends

Rondônia is located almost entirely within the Legal Amazon and covers 243,044 km. The state is one of the most deforested in the Brazilian Amazon, with tree cover of 157,601 km 2 in 20132. Deforestation in Rondônia is largely a result of colonization projects and the expansion of beef and soya production. As global demand grew in the 1960’s, beef production in Rondônia expanded. Road-building enabled population growth and increased agricultural production. Consequently, state deforestation rates rocketed from the 1980s as land was cleared for pastures, croplands, and urban development. By 1991, Rondônia’s cattle herd was one of the largest in Brazil, producing almost twice as much beef as the state consumed 3. Rondônia’s beef industry continued to expand during subsequent decades, with cattle herds in the Brazilian Amazon growing by 23 to 33% between 1995-96 and 2006 4. Large-scale soy production began in Brazil in the 1990s. By the time the crop arrived in Rondônia, many areas suitable for soy production were already utilised as cattle pasture. Soy expansion in the state thus often occurred on pastures, displacing cattle production and indirectly causing deforestation. This trend became especially pronounced following the introduction of the Soy Moratorium in 2006, which saw Brazil’s largest soya companies commit to avoid sourcing soya from land deforested after 2006. 5 Deforestation rates in Rondônia remained high until 2005, when forest loss decreased rapidly due to a combination of policy initiatives, industrial action, and decreased consumer demand during the economic crisis6.

State Policy and Initiatives

The state government of Rondônia has supported the implementation of the Forest Code through creating a registry of rural properties and promoting environmental licensing to create compliance with land use regulations. The state is also a member of the Governors’ Climate and Forests Task Force (GCF), which seeks to reduce emissions from deforestation and establish governance frameworks through the collaboration of its 29 member states and provinces. However, most action on protecting Rondônia’s remaining forest is legislated through national policy.

Cabixi is a municipality in the Brazilian state of Rondônia in the North-West region of Brazil.

Unscaled Deforestation Ratio = 0.009

MEDIUM5% of your Animal Feed contributed to deforestation

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Conclusions

• One example of OeRC model of applied research and application design model reuse

• Reusing technology from one project to another leads to impactful projects showcasing relevance of academic work

• Building significant long term relationships with both academic collaborators and industrial partners

• OeRC is unique, these types of translation between different application domains with translation in the academic input within the project wouldn’t happen in another more traditional department.

Astro Physics


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Smart Metering

Data Mashing

Energy Theft Detection

Machine learning and Analytics

Energy System Data


New model development Deforestation

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Thank youQuestions?