Smart Energy Research Lab: Energy use in GB domestic buildings 2021

Variation in annual, seasonal, and diurnal gas and electricity use with weather, building and occupant characteristics

The SERL Observatory Dataset: Longitudinal Smart Meter Electricity and Gas Data, Survey, EPC and Climate Data for over 13,000 Households in Great Britain

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 was 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 was collected, the variables available and the representativeness of the sample compared to national estimates. It is intended as a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it

The impact of COVID-19 on household energy consumption in England and Wales from April 2020 to March 2022

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

Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)

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

A stability analysis of thermostatically-controlled loads for power system frequency control

Thermostatically-controlled loads (TCLs) are a flexible demand resource with the potential to play a significant role in supporting electricity grid operation. We model a large number of identical TCLs acting autonomously according to a deterministic control scheme to provide frequency response as a population of coupled oscillators. We perform stability analysis to explore the danger of the TCL temperature cycles synchronising: an emergent phenomenon often found in populations of coupled oscillators, and predicted in this type of demand response scheme. We take identical TCLs as it can be assumed to be the worst case. We find that the uniform equilibrium is stable and the fully synchronised periodic cycle is unstable, suggesting that synchronisation might not be as serious a danger as feared. Then detailed simulations are performed to study the effects of a population of frequency-sensitive TCLs acting under real system conditions using historic system data, and the potential reduction in frequency response services required from other providers is determined, for both homogeneous and heterogeneous populations. For homogeneous populations, we find significant synchronisation, but a very small amount of diversity removes the synchronisation effects. In summary, we combine dynamical systems stability analysis with large-scale simulations to offer new insights into TCL switching behaviour

The dynamics of thermostatically controlled loads for power system frequency control

Major changes are under way in our power grids. Until very recently, a few hundred, very large, dependable fossil-fuelled power stations were supplying power to consumers whose only role was to use energy whenever they wanted. Today we have wind farms, solar farms, solar panels on millions of roofs, smart metering. Electric vehicles are on the rise and storage technologies are developing rapidly. Achieving a low-carbon, affordable, and secure electricity system, the so-called `energy trilemma,' presents many challenges and opportunities. As energy becomes more dependent on volatile resources such as the wind and sun, flexibility will become increasingly important for maintaining system security at palatable costs. One new source of flexibility could come from domestic appliances. Thermostatically-controlled loads (TCLs), such as fridges, freezers, air-conditioners and hot-water tanks are effectively energy stores that can be adapted to meet the needs of the grid with negligible impact on consumers. By allowing their operating set points to vary (a little) according to the electricity frequency, they could provide a valuable resource to the grid. However, a thorough understanding of their potential to exhibit synchronisation will be needed to understand and mitigate against the potential risks of a decentralised response provider. In this thesis I outline the operation of the electricity grid in Great Britain and describe the existing research into using TCLs for demand-side response. I present a new continuum model for a population of deterministic frequency-sensitive TCLs that is sufficiently tractable to allow for our stability analysis. I also solve for the long-term behaviour of a fully synchronised group of TCLs and analyse its stability to splitting into two groups, and hypothesise about the stability of N groups. Using system data from National Grid, the operation of the GB electricity system is simulated over ten-day periods with, and without, a population of fridges providing frequency response to determine their impact. I find that synchronisation issues should always be expected when the fridge population is identical, but with even very low levels of parameter diversity, such issues are eradicated in our simulations. Given the inherent diversity in a population of TCLs, this research shows that decentralised, deterministic control schemes are a viable option for using TCLs for frequency response, and that such a scheme could provide a valuable resource

Smart Energy Research Lab Observatory Data, 2019-2020: Secure Access (Edition 2)

The vision of the Smart Energy Research Lab (SERL) is to deliver a unique data resource harnessing the benefits of smart meter data for research. The portal will transform Great Britain&#39;s energy research through the long-term provision of high quality, high-resolution energy data that will support the development of a reliable evidence base for intervention, observational and longitudinal studies across the socio-technical spectrum. The first edition of the Smart Energy Research Lab Observatory Data, 2019-2020: Secure Access relates to 1,700 participant households recruited during SERL&rsquo;s pilot phase in August-September 2019</span

Increasing response rates and improving research design: Learnings from the Smart Energy Research Lab in the United Kingdom

Obtaining high-resolution energy consumption data from a large, representative sample of homes is critical for research, but low response rates, sample bias and high recruitment costs form substantial barriers. The widespread installation of smart meters offers a novel route to access such data, but in countries like Great Britain (GB) consent is required from each household; a real barrier to large-scale sampling. In this paper we show how certain study design choices can impact the response rate for energy studies requesting access to half-hourly smart meter data and (optional) survey completion. We used a randomised control trial (RCT) with a 3×2×2 factorial design; 3 (including none) incentive groups ×2 message content/structures ×2 ‘push-to-web’ treatment groups. Up to 4 mailings (letters) were sent to 18,000 addresses, recruiting 1711 participants (9.5% response rate) in England and Wales. The most effective strategy offered a conditional £5 voucher and postal response options in multiple mailings (compared to only once in the push-to-web approach, although at the expense of far fewer online signups). Motivational headlines and message structure were also found to be influential. Reminders increased response but a 4th mailing was not cost effective. Our results and recommendations can be used to help future energy studies to achieve greater response rates and improved representation. UK-based researchers can apply to use our longitudinal smart meter and contextual datasets.</p

Smart Energy Research Lab Observatory Data, 2019-2021: Secure Access (Edition 3)

The vision of the Smart Energy Research Lab (SERL) is to deliver a unique data resource harnessing the benefits of smart meter data for research. The portal will transform Great Britain&#39;s energy research through the long-term provision of high quality, high-resolution energy data that will support the development of 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 data High 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 aims to recruit around 10,000 households to be 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. 26 April 2021: For the latest edition (April 2021) all SERL data up to and including 31 October 2020, which includes participant households recruited in Waves 1 and 2 were made available. </span