• Energy Research

The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes were collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data were collected, the variables available and the representativeness of the sample compared to national estimates. It is intended to be a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it.

To contain the spread of Covid-19, governments across the world imposed partial or complete lockdowns. National energy demand decreased in periods of lockdowns; however, as people spent more time at home, residential energy use likely increased. This paper reports the results of a UK survey study ('N' = 1016 participants) about their energy-use practices during the first lockdown in March 2020. The results indicated that self-reported heating behaviours did not substantially change during lockdown. Regarding appliance use, in particular the duration of usage for televisions and computing equipment has increased and has spread more over the day. Being less able to manage financially was correlated with a greater usage of the smart meter in-home display and a greater attempt to save energy was positively correlated with greater usage of the in-home display, though correlations were small. In summary, the results indicate that home energy-use behaviours, in particular around heating, did not change as much as might have been expected, which might at least partly be explained by the comparatively warm weather during the first lockdown. Corroborating the survey findings with actual energy data is the next essential step to understand findings in more detail. 'Policy relevance' Governments are developing policies to support the transition to net zero. Covid-19 has accelerated the transition in behaviours such as home working which may result in a ‘new normal’ energy behaviour and will need to be taken account when planning for net zero. Insights into the changes in behaviour during lockdown indicate it would be oversimplified to assume that electricity and gas use have increased in all homes because of a stay-at-home order. Self-reported heating did not change, whereas electrical appliance usage increased. The sample composition of the household is important for understanding the energy implications. In this study, about half the households did not spend more time at home during lockdown as judged on their work status. In-home displays may support energy saving behaviour, particularly for those doing less well financially. Hence, promoting their use should be a key consideration when installing smart meters.

[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.

Abstract Sovacool et al. (2017) consider and critique the Smart Metering Implementation Programme (SMIP) in the United Kingdom. In this response we highlight a number of areas where we believe it would have been helpful for readers to see additional information. These relate to three areas: how the authors reported on the methods used in their study; how the Programme is addressing technical challenges; and how the Programme is engaging with people in vulnerable situations. We also point out areas where we see emerging challenges, such as engaging non-traditional stakeholders, and how organizations which want to use smart meter data in the public interest might encounter barriers in meeting the demands of the Smart Energy Code.

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 carbon intensity of the electricity used to charge an electric vehicle (EV) is dependent on when in the day charging occurs. However, persuading EV owners to adopt incentives to charge during off-peak hours is challenging. Here we show that governments could exploit the ‘window of opportunity’ created when people purchase their first EV to promote time-of-use tariffs. Email recipients (n = 7,038 EV owners) were more likely to click-through to an information webpage when the email emphasized specific reductions in home-charging costs versus general bill savings. However, the ‘window of opportunity’ for maximizing potential adoption is short; email open rates declined from over 70% immediately after purchase to 40% for recipients owning their EV for over three months. These results demonstrate the potential of prompts to change behaviours for which opt-out enrolment (where enrolment is automatic unless people explicitly opt out) would be unethical or less effective. Electric vehicles are only as green as the electricity used to charge them, but owners tend to charge vehicles at times of peak use. This study shows that tailored emails increase engagement with information about time-of-use tariffs, with maximal effects within the first three months of ownership.

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.

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.