This document discusses big data in the context of energy usage and infrastructure. It addresses challenges in analyzing, storing, and accessing big data. It provides examples of how analytics can be used to classify energy costs, cluster load profiles, and analyze the impact of time-of-use pricing on commercial energy consumption. Issues discussed around big data include high costs of storage and transfer, lack of data standards, and ensuring data is uniformly accessible to stakeholders while protecting privacy.

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IWWORKING with
Infrastructure Creation of Knowledge and Energy strategy Development
http://www.energy.ox.ac.uk/wicked/
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Generating Insight from Big Data
David Wallom

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What do you consider Big Data to be?
Big data is a broad term for data sets so
large or complex that traditional data
processing applications are inadequate.
Challenges include analysis, capture, data
curation, search, sharing, storage, transfer,
visualization, and information privacy.

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14 July 2015
Scale matters
for problems and solutions
in the built environment
“stock” at the city, national, international scale
The building
(or leaseable unit)

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The Challenge
 In UK, £1.7 Bn of energy
consumed is not managed
 Large businesses waste around
15% of energy due to lack of
efficiency measures
 £5Bn spent on new buildings
each year, which use 2-3 times
more than designed
 Return on investment (ROI) of
48% available for many measures
 Effective management will
reduce risk and increase security
of supply
 Market transformation via
  Awareness of problem
  Understanding of technical
performance
  Opportunities for socio-
technical change
14 July 2015
Source: Sweett Group, Verco, UCL, &
Energy Foundation. 2014. Operational
Energy Use and the Use of 'Bigger, Better
Data". GCB230. Green Construction Board:
London
The Opportunity

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What can analytics say about energy usage?
 Turning Data into Actionable Information;
 Supporting network functions
 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 load profiles, determining behaviour type and/or
consumer response
 Determining fundamental drivers of energy consumption

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HPC Engine and Storage
Next Generation Infrastructure
The Smart Grid
High Speed Communications System
Service
Restoration
Voltage
Control
Condition
Monitoring
/Data
Mining
Distribution
System
State
Estimation
Distribution Management System
Who other than the consumer uses data?
Smart Distribution relies on multiple
data sources
 Distribution System State Estimation
 Service Restoration Algorithm
 Condition Monitoring
 Voltage Control

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Clustering load profiles using Bayesian analytic
techniques

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Normalised daily
power demand
profiles for all
businesses by sector
Commercial energy consumption and real time
pricing
 Analyse the impact of introduction of time-of-use and real-
time pricing strategies

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How is big data best collected and stored, and
who ought to have access?
 Data costs, to both move and store, BIG DATA costs A LOT!
 Maximise value of data once you have it by making it uniformly accessible to
all stakeholders and their business functions
 Increasingly common that energy information is handled on behalf of the
retailer by a third party contractor.
 Lack of standards in all aspects of data formats
 Changing energy management provider is an epic task, will it become the same
for domestics?
 Example, we have partnerships within WICKED with four different retailers, four
different configurations of data and inconsistent metadata.
 Domestic ‘Big Data’ is going to have to be simple to maximise value
 What is the status of price elasticity?
 How accurately can we connect usage with activity?
 What is the idea resolution/volume/sample size?