Ben Anderson

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.

[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.

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.

One of the key challenges to greater renewable electricity supplies is the temporal mismatch between non-dispatchable renewable sources and peaks in electricity demand. In addition, increased electrification coupled with the de-carbonisation of electricity generation is likely to increase the scale of demand peaks. This could force investment in carbon-intensive peaker generation or capital intensive storage capacity as well as additional transmission and distribution network capacity which may then be substantially underutilised. Whilst considerable effort has been devoted to testing a range of demand response interventions to reduce or shift consumption, less attention has been given to the ability of certain appliances to permanently reduce demand at peak through energy efficiency. In this paper, we use a published model of future energy-efficient lighting uptake together with multi-year measured lighting demand data from a sample of residential households to model the potential power (MW) and energy (MWh) reductions of a ‘business as usual’ rate of efficient lighting adoption. Our estimates suggest that whilst lighting comprises ~ 4% of overall New Zealand annual electricity consumption, it comprises up to 12% of evening peak electricity consumption in winter. As a result, we estimate that by 2029, more efficient residential lighting could reduce New Zealand’s total annual demand by 1 TWh and reduce the highest winter evening peaks (at 17:00) by at least 500 MW (9%). The winter evening demand reduction would be roughly equivalent to avoiding the need for additional generation capacity of the scale of New Zealand’s Huntly Power Stations 1–4 (coal/gas) plus the Stratford peaker plant (gas open-cycle) and has clear implications for any electricity system that is intending to transition towards ~ 100% renewable generation at least cost.