The report, ‘Smart Meter Data for Measurement and Identification of Energy Poverty’, by HouseInc, explores the use of smart meter data to improve the measurement and identification of energy poverty.
Energy poverty refers to a condition where a household is unable to access or afford essential energy services due to a combination of low income, high– energy costs and inefficient energy infrastructure. In the EU, 40% of the population qualifies as energy poor, according to at least one energy poverty indicator (Maier & Dreoni, 2024). Identifying households facing energy poverty is crucial in solving energy poverty as being able to identify more vulnerable households is the first step in developing effective and sustainable solutions.
The problem with current methods for measuring energy poverty
Existing methods to identify energy poverty often rely on household surveys, which are costly, slow, and prone to recall bias. In addition, commonly used approaches to allocate support, such as blanket subsidies based on broad eligibility criteria like age or income, can be inaccurate and result in the misallocation of resources, as they may not effectively target households actually experiencing energy poverty.
The current roll-out of smart meters presents an opportunity to improve the measurement and identification of energy poverty. The study investigates the use of smart meter data to improve the measurement and identification of energy poverty by conducting a detailed case study using the SERL Observatory dataset with data from 13,000 households in Great Britain equipped with smart meters and with linked contextual data about the household and building physical characteristics.
What are the benefits of smart meter data?
Smart meter data provides high-resolution, accurate and longitudinal data on energy use, prices, and expenditure. It enables cost-effective, timely, and scalable data collection, addressing the limitations of survey-based methods. In addition, smart meter data could be used to train machine learning models to identify energy-poor households.
A complimentary tool to evaluate energy poverty
Smart meter-based indicators such as the Actual Expenditure Energy Poverty (AEEP), (which classifies households as energy poor if they spend 10% or more of disposable income on energy bills) have advantages over traditional methods:
- reduce recall biases
- has high temporal resolution
- allows for longitudinal tracking
- cost-effective
- easier to implement – where smart meter infrastructure exists
The study found the AEEP indicator useful as a tool to measure energy poverty where smart meter data is available. This is because it is easier to implement compared to other expenditure-based energy poverty indicators, and this opens more opportunities for improving the understanding of energy poverty and tackling it effectively.
The report recommends:
- Governments and stakeholders should leverage smart meter data for energy poverty monitoring and program evaluation.
- The roll-out of smart meters should be accelerated, and access to smart meter data for public interest research should be expanded.
- Further research is needed to develop machine learning models for energy poverty identification.
Smart meter data offers an opportunity to improve the measurement, identification, and evaluation of energy poverty. The report advocates for the broader use of smart meter data, the development of machine learning models, and the establishment of Energy Demand Observatories similar to the SERL Observatory across the EU to tackle energy poverty effectively and enable innovative energy research more generally.
To find out more details, read the report here
Plus, find here a graphic about the benefits of smart meter data
