• Energy Research

In October 2022 British households entered a heating season amidst exceptionally high energy prices – squeezing household incomes and increasing fuel poverty. This study analyses electricity and gas consumption in 5594 households from October 2022 to March 2023 using XGBoost counterfactual models trained on historic data. With survey data collected in early 2023 we investigate how consumption reduction correlated with energy-saving actions, household and dwelling characteristics, and indicators of underheating and fuel poverty.Our analysis showed that electricity consumption was 8.4% lower and gas consumption 10.8% lower than the previous winter (accounting for weather), saving consumers around £29/month. Despite this and a government subsidy, energy bills were still around £34/month higher than the previous winter (£158/month (median); £500/month (95th percentile)); price elasticity was −0.10 for electricity and −0.07 for gas consumption. Greatest consumption reduction correlated with largest reported changes to heating practices, in particular heating for fewer hours and turning thermostats down lower. We find evidence of greater fuel poverty and underheating among the greatest energy reducers.This paper presents novel methods for analysing energy saving using smart meter data for changes without a control group, plus novel findings related to short-term price elasticity and the energy-saving impacts of behaviour change.

The Smart Energy Research Lab (SERL) Observatory facilitates a broad range of energy demand research and is a unique data resource for research where access to high resolution, large scale energy data linked to relevant contextual data is required. Further information about SERL can be found on the Smart Energy Research Lab website.This dataset of aggregated statistics is available under standard Safeguarded (End User Licence) access conditions. It contains over 2.5 million rows of data and describes domestic gas and electricity energy use in Great Britain 2020-2023 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. This aggregated dataset can be used, for example, to show how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day); and can be broken down by occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile. Secure Access dataA more detailed set of SERL data, including smart meter data and additional contextual data, is available under restricted Secure access conditions under SN 8666: Smart Energy Research Lab Observatory Data: Secure Access. It is a longitudinal dataset containing records from August 2019, with updates provided to researchers on a (roughly) quarterly basis. Users should download this safeguarded access statistical study first to see whether it is suitable for their needs before considering an application for the Secure dataset.The second edition (May 2024) includes summaries of daily average energy use in a data file for 2020-2023, and summaries of half-hourly average energy use in four data files for 2020-2023, as well as an accompanying technical document. Energy (electricity and gas) consumption in households across Great Britain. Abstract copyright UK Data Service and data collection copyright owner. Main Topics:

This analysis compares the difference between the Energy Performance Certificate (EPC)-modelled and smart-meter measured annual energy use on a like-for-like basis in 1,374 gas-heated British households from the Smart Energy Research Lab (SERL) Observatory. EPCs and metered energy use were converted to primary energy use intensity (PEUI) to provide a comparison of the same quantity for the first time.We show that EPCs predict significantly more energy use than metered in homes in Great Britain. EPC bands A and B show no statistically significant difference, but all other bands show a significant gap which increases as EPC rating worsens. The PEUI gap widens from -26 kWh/yr/m2 (-8%) for band C to -276 kWh/y/m2 (-48%) for bands F and G. Unlike previous research, we show that the difference persists in homes matching the EPC-model assumptions regarding occupancy, thermostat set-point and whole-home heating; suggesting that occupant behaviour is unlikely to fully explain the discrepancy. EPCs are a core tool in the residential energy sector, and the gap between EPC-modelled and metered energy use could have a significant impact on policy, research, and industry. Future research should investigate disaggregated components of energy use, the underlying thermal model, and assumptions regarding building characteristics.

[Update: a substantially modified version of this paper has been published in the journal Energy & Buildings https://doi.org/10.1016/j.enbuild.2022.111845. Please refer to and cite the published paper rather than this pre-print.] This paper investigates factors associated with variation in daily total energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for a sub-sample of 617 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel.Linear mixed effects modelling resulted in marginal/conditional R2 of 0.68/0.83 and root mean squared error of 17.7 kWh/day, for daily gas and electricity use combined. Increased daily energy consumption was significantly associated (p-value<0.05) with: households living in buildings with larger floor area, more rooms, that are older, have lower energy efficiency, and experience colder or less sunny weather; households with more adult occupants, more children, older adult occupants, fewer adults with qualifications, higher heating temperature setpoints, that do not try to save energy, and that do not put on more clothes rather than turning the heating on. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data which has recently been updated to include over 13,000 households from across Great Britain.

Energy performance certificates (EPCs) are ratings of domestic building energy performance mandated across the European Union. Their aim is to provide a reliable assessment of a building’s energy performance whilst accounting for non-building effects such as weather and occupancy. Current rating methods, based on theoretical calculations, can introduce significant error from an inability to estimate real building performance. Other methods using real energy data cannot isolate building performance from other effects due to low data resolution. The installation of smart meters in large proportions of the housing stock in European Union member states presents an opportunity. Harnessing high-resolution energy data can create or inform building energy performance ratings with reduced error and at scale. This critical review explores the challenges and opportunities of using smart meter data in building energy ratings, focusing primarily on quantifying the thermal performance of the building and heating system. The research gaps in this emerging field are identified, including: demonstrating that the rating is truly independent of the behaviour of specific occupants; the additional data inputs that add most value in combination with smart meter data; and reducing uncertainty whilst limiting the complexity of the measurement and calculation. 'Practice Relevance' Increasing evidence shows current EPCs are unreliable. This unreliability can affect their usefulness to householders and the provision of evidence for policy decisions. The incorporation of metrics constructed from smart meter data can provide a rating of building thermal performance that better reflects the actual performance of a dwelling. The potential advantages of incorporating smart meter data would improve the reliability of building energy ratings and quantify the rating uncertainty on a per dwelling basis, which would be useful for risk assessment to inform finance and retrofit decisions. Technical challenges are identified and explained for the inclusion of smart meter data. These are summarised as follows: ensuring that ratings remain independent of occupant behaviours/practices; and identifying which additional data inputs increase reliability and enable more informed retrofit decision-making whilst keeping the rating cost low and the calculation complexity tractable.

The Smart Energy Research Lab (SERL) delivers a unique energy data resource to the UK research community that enables a broad range of multi-disciplinary, socio-technical research relating to energy consumption in domestic buildings. The SERL Observatory is transforming Great Britain's energy research through the long-term provision of high quality, high-resolution energy data that provides a reliable evidence base for intervention, observational and longitudinal studies across the socio-technical spectrum. The goals of the Smart Energy Research Lab are to provide: A trusted data resource for researchers to utilise large-scale, high-resolution energy data An effective mechanism for collecting and linking energy data with other contextual dataHigh quality data management to ensure fit-for-purpose data are provisioned to researchers Participant recruitment began in August 2019. Approximately 1,700 participants were recruited from central and southern England and from Wales as part of a pilot study that tested different recruitment strategies. The second recruitment wave took place in August-September 2020, and the third wave at the start of 2021. SERL recruited over 13,000 households which are regionally representative across England, Scotland and Wales. Recruitment is also designed to be representative of each Index of Multiple Deprivation (IMD) quintile; an area-based relative measure of deprivation. For the latest edition (released in May 2024), all SERL data up to and including 31st December 2023 were made available. Users should note that this is the 6th edition of SERL data that has been released, though the citation may refer to the 7th edition. All code provided with the data is now managed on the SERL GitHub website. Smart meter data: Daily and half-hourly energy (electricity and gas) consumption dataTariff dataAdditional smart meter technical data Contextual data: SERL survey (initial) completed by participant households on sign-up providing data on household information and building characteristics.Energy Performance Certificate (EPC) dataWeather dataSERL Covid-19 survey: sent to wave 1 participants in May 2020 to understand their circumstances during the first lockdownSERL survey (Follow-up 2023): sent to all active participants in early 2023 to investigate the impact of rapidly rising energy costs in 2022/2023 SERL data will be updated and made available to researchers on a quarterly basis. SERL is an evolving data resource and thus new editions of the data might include: additional records – more smart meter data, since the previous editionadditional participants – more participants recruited since the previous releaseadditional variables – where new variables become available to SERL Further information about SERL can be found on serl.ac.uk and in the associated documentation. The 'Key Documents' section of the SERL website, which links to all publications that use SERL data, can be found at serl.ac.uk/key-documents. If you do not see your SERL-data publication listed, please contact the SERL team via info@serl.ac.uk. For the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 data users should note that neither the European Commission nor the European Centre for Medium-Range Weather Forecasts will be held responsible for any use that may be made of the Copernicus information or data it contains. The Energy Performance of Buildings Data is also included and users must read and abide by the Copyright Information Notice, provided by the Department for Levelling Up, Housing and Communities, that covers the use of Royal Mail information and non-address data provided under the Open Government Licence v3.0.For the latest edition (released in November 2024), all SERL smart meter and climate data have been updated to June 2024. Users should note that this is the 7th edition of SERL data that has been released, though the citation may refer to the 8th edition. The SERL Observatory panel provides data primarily relating to energy demand and consumption in domestic buildings in Great Britain. Abstract copyright UK Data Service and data collection copyright owner. Main Topics:

This study investigates typical domestic energy demand profiles and their variation over time. It draws on a sample of 13,000 homes from Great Britain, applying k-means cluster analysis to smart meter data on their electricity and gas demand over a three-year period from September 2019 to August 2022. Eight typical demand archetypes are identified from the data, varying in terms of the shape of their demand profile over the course of the day. These include an ‘All daytime’ archetype, where demand rises in the morning and remains high until the evening. Several other archetypes vary in terms of the presence and timing of morning and/or evening peaks. In the case of electricity demand, a ‘Midday trough’ archetype is notable for its negative midday demand and high overnight demand, likely a combination of the effects of rooftop solar panels exporting to the grid during the day and overnight charging of electric vehicles or electric storage heating. The prevalence of each archetype across the sample varies substantially in relation to different temporally-varying factors. Fluctuations in their prevalence on weekends can be identified, as can Christmas Day. Among homes with gas central heating, the prevalence of gas archetypes strongly relates to external temperature, with around half of homes fitting the ‘All daytime’ archetype at temperatures below 0°C, and few fitting it above 14°C. COVID-19 pandemic restrictions on work and schooling are associated with households’ patterns of daily demand becoming more similar on weekdays and weekends, particularly for households with children and/or workers. The latter group had still not returned to pre-pandemic patterns by March 2022. The results indicate that patterns of daily energy demand vary with factors ranging from societal weekly rhythms and festivals to seasonal temperature changes and system shocks like pandemics, with implications for demand forecasting and policymaking.

The COVID-19 pandemic changed the way people lived, worked, and studied around the world, with direct consequences for domestic energy use. This study assesses the impact of COVID-19 lockdowns in the first two years of the pandemic on household electricity and gas use in England and Wales. Using data for 508 (electricity) and 326 (gas) homes, elastic net regression, neural network and extreme gradient boosting predictive models were trained and tested on pre-pandemic data. The most accurate model for each household was used to create counterfactuals (predictions in the absence of COVID-19) against which observed pandemic energy use was compared. Median monthly model error (CV(RMSE)) was 3.86% (electricity) and 3.19% (gas) and bias (NMBE) was 0.21% (electricity) and -0.10% (gas). Our analysis showed that on average (electricity; gas) consumption increased by (7.8%; 5.7%) in year 1 of the pandemic and by (2.2%; 0.2%) in year 2. The greatest increases were in the winter lockdown (January – March 2021) by 11.6% and 9.0% for electricity and gas, respectively. At the start of 2022 electricity use remained 2.0% higher while gas use was around 1.9% lower than predicted. Households with children showed the greatest increase in electricity consumption during lockdowns, followed by those with adults in work. Wealthier households increased their electricity consumption by more than the less wealthy and continued to use more than predicted throughout the two-year period while the less wealthy returned to pre-pandemic or lower consumption from summer 2021. Low dwelling efficiency was associated with a greater increase in energy consumption during the pandemic. Additionally, this study shows the value of different machine learning techniques for counterfactual modelling at the individual-dwelling level, and our approach can be used to robustly estimate the impact of other events and interventions.