Energy Theft Reduction Strategy

What is Energy Theft?

Energy theft is any form of illegal extraction of gas and electricity, whereby the full amount of energy used is not recorded and paid for. This usually involves ‘tampering’ the meter to prevent accurate recording. As it is incredibly dangerous to interfere with an energy supply, as well as the increasing costs for consumers, under the Retail Energy Code, we are required to work with the energy industry to progress an Energy Theft Reduction Strategy.

What is RECCo’s Energy Theft Reduction Strategy?

Our overall aim is to reduce the extent of energy theft in the GB energy industry, thereby reducing the cost to bill paying consumers, and also mitigating the safety risks. However, there is currently no reliable measure of the scale of the problem – usually tracing back to Ofgem’s estimate of £400-500m from several years ago. Ofgem recognised that a more robust estimate would be required as part of any business case to justify further substantial investment. The REC, therefore, places an obligation on RECCo to procure a Theft Estimation Methodology. RECCo has commissioned one of the Code Managers to produce the Theft Estimation Methodology, aimed at providing a figure on which Industry parties could agree, but could be re-run periodically to determine whether we are successfully mitigating the problem.

While the energy theft reduction strategy will be further developed as part of our overall strategy and forward work plan over the coming years, some of the elements are already in place and have been migrated from legacy code arrangements:

The Energy Theft Tip-Off Service (for more information click here); and
The Energy Theft Detection Incentive Schemes (for more information click here).

Theft Estimation Methodology Project

We issued a request for proposals from potential service providers who could support the development of the Theft Estimation Methodology. We received four submissions, which were evaluated by RECCo’s executive team members and subject matter experts. They concluded that Capgemini offered the strongest proposal.

We have been working alongside Capgemini to progress the project and create a plan to develop the methodology.

The initial stage of the plan focussed on determining what data was available. Now that this stage has been completed, we are moving on to the development of the methodology. Capgemini have developed the below:

The plan to develop the Theft Estimation Methodology is as follows:

Method 1 – WHOLE SYSTEM ENERGY BALANCE (AGGREGATE)

Aim: to understand the order of magnitude for potential volume of theft as a baseline

Sum of all energy fed into the distribution/transportation systems minus the sum of all reported consumption, the also subtract modelled ‘Technical losses’.

Techniques: data aggregation, confidence estimation, uncertainty propagation.

Primary data:

Gas and electricity settlement data;
Performance assurance data;
Smart meter data.

Method 2 – NETWORK CENTRIC ENERGY BALANCE (GRANULAR)

Aim: to understand the order of magnitude for potential volume of theft in geographical segments. Compares the energy entering and exiting a section of network, working at a more granular level than “whole system” above.

Sample several network segments for which the input and sufficient endpoint energy metering data is available today

Techniques: subgraph sampling, Bayesian inference, uncertainty estimation.

Primary data:

DNO/GT substation, feeder or similar meter data;
Supplier meter data flow for those networks;
Property data;
Company categorisation;
Former TRAS data;
ETTOS data;
Technical loss/leakage date.

Method 3 – PRIOR THEFT INCIDENTS EXTRAPOLATION (SEGMENTED)

Aim: to identify patterns and drivers of theft to extrapolate for the entire network. Uses the historical records of theft detection (and suspicion) to extrapolate the likely volume of similar theft across the population. The key to this will be meaningful characterisation and segmentation of the theft incidents.

Techniques: feature engineering, feature importance analysis, ML regression, hypothesis testing.

Primary data:

Former TRAS data;
Cannabis market trend data, etc;
Other specific theft;
Deprivation data;
Energy poverty trends;
Energy consumptions trends;
Theft data from other sectors (e.g. water).

Method 4 – TREND EXTRAPOLATION

Aim: to understand time-based effects in theft and propose forecasting methods for theft volumes. Modelling hypotheses around the driving factors for energy theft and project how they are likely to affect the volumes of theft

Supports the periodic re-estimation requirement and will benefit from feedback of detection results in the future.

Techniques: temporal feature engineering, trend/seasonality analysis, autoregressive forecasting with exogenous variables.

Primary data: As per method three.

The progress of this project is heavily dependent upon the provision of timely data. Therefore, although an agile approach is being taken, timelines may need to be adapted in light of progress. The project is being structured around a small number of short sprints as outlined below:

Sprint	Delivery Activities
0 – Kick-off	Hypothesis Development – the refinement and documentation of the hypotheses outlined above, with a more detailed articulation of the required data sets for each Data Availability Assessment – assurance activity that the delivery team has access to the required data sets outlined as part of Hypothesis Development Data Requirement Documentation – articulation of missing data requirements and preparation for issuing RFIs
1 – Convergence on the Ideal Method	Data Acquisition & Transformation – the acquisition, quality assessment, cleaning, aggregation and transformation of the available data from disparate sources Data Analysis & Method Convergence – exploratory analysis of data, testing of available methods based on the available data sets and convergence on an ideal method Data Requests Issued – any requirements for additional data requested through RECCo
Take stock and if necessary re-plan
2 – Method-specific in-depth analysis	Data Acquisition & Transformation – the acquisition, quality assessment, cleaning, aggregation and transformation of newly available data from requests Data Analysis & Model Development – exploratory analysis of data, testing of available methods based on the available data sets
3 – Model development	Model Refinement – implementation and validation of the final TEM
Close – Business Case Development	Documentation – documentation of the final estimation model including data sources, modelling methodologies, assumptions and user guides

If you would like any more information on this project please contact the team at info@retailenergycode.co.uk.

Theft Strategy – Events

Past Events

Upcoming Events

Jon Dixon and Aiyesha Andrade attended the 2022 conference of the UK Revenue Protection Association to provide an update on our Theft Reduction Strategy, covering both improvements to existing theft related services and seeking feedback on some of the newer initiatives that are being explored.

RECCo is planning to host an Energy Theft Reduction Workshop in person. More details are to be announced soon!