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The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets

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  • Lesley Thomson

    (Institute of Sustainable Built Environment, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh EH14 4AS, UK)

  • David Jenkins

    (Institute of Sustainable Built Environment, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh EH14 4AS, UK)

Abstract

The availability of empirical energy data from Advanced Metering Infrastructure (AMI)—which includes household smart meters—has enabled residential energy demand to be characterised in different forms. This paper first presents a literature review of applications of measured electricity, gas, and heat consumption data at a range of temporal resolutions, which have been used to characterise and develop an understanding of residential energy demand. User groups, sectors, and policy areas that can benefit from the research are identified. Multiple residential energy demand datasets have been collected in the UK that enable this characterisation. This paper has identified twenty-three UK datasets that are accessible for use by researchers, either through open access or defined processes, and presents them in an inventory containing details about the energy data type, temporal and spatial resolution, and presence of contextual physical and socio-demographic information. Thirteen applications of data relating to characterising residential energy demand have been outlined in the literature review, and the suitability of each of the twenty-three datasets was mapped to the thirteen applications. It is found that many datasets contain complementary contextual data that broaden their usefulness and that multiple datasets are suitable for several applications beyond their original project objectives, adding value to the original data collection.

Suggested Citation

  • Lesley Thomson & David Jenkins, 2023. "The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets," Energies, MDPI, vol. 16(16), pages 1-29, August.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6069-:d:1220605
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    References listed on IDEAS

    as
    1. Jenkins, David & Simpson, Sophie & Peacock, Andrew, 2017. "Investigating the consistency and quality of EPC ratings and assessments," Energy, Elsevier, vol. 138(C), pages 480-489.
    2. Viegas, Joaquim L. & Vieira, Susana M. & Melício, R. & Mendes, V.M.F. & Sousa, João M.C., 2016. "Classification of new electricity customers based on surveys and smart metering data," Energy, Elsevier, vol. 107(C), pages 804-817.
    3. Yunbo Yang & Rongling Li & Tao Huang, 2020. "Smart Meter Data Analysis of a Building Cluster for Heating Load Profile Quantification and Peak Load Shifting," Energies, MDPI, vol. 13(17), pages 1-20, August.
    4. Love, Jenny & Smith, Andrew Z.P. & Watson, Stephen & Oikonomou, Eleni & Summerfield, Alex & Gleeson, Colin & Biddulph, Phillip & Chiu, Lai Fong & Wingfield, Jez & Martin, Chris & Stone, Andy & Lowe, R, 2017. "The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial," Applied Energy, Elsevier, vol. 204(C), pages 332-342.
    5. Bagge, Hans & Johansson, Dennis, 2011. "Measurements of household electricity and domestic hot water use in dwellings and the effect of different monitoring time resolution," Energy, Elsevier, vol. 36(5), pages 2943-2951.
    6. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    7. Francesco Mancini & Benedetto Nastasi, 2019. "Energy Retrofitting Effects on the Energy Flexibility of Dwellings," Energies, MDPI, vol. 12(14), pages 1-19, July.
    8. Wiesmann, Daniel & Lima Azevedo, Inês & Ferrão, Paulo & Fernández, John E., 2011. "Residential electricity consumption in Portugal: Findings from top-down and bottom-up models," Energy Policy, Elsevier, vol. 39(5), pages 2772-2779, May.
    9. Alexander Martin Tureczek & Per Sieverts Nielsen, 2017. "Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data," Energies, MDPI, vol. 10(5), pages 1-19, April.
    10. Huebner, Gesche M. & Hamilton, Ian & Chalabi, Zaid & Shipworth, David & Oreszczyn, Tadj, 2015. "Explaining domestic energy consumption – The comparative contribution of building factors, socio-demographics, behaviours and attitudes," Applied Energy, Elsevier, vol. 159(C), pages 589-600.
    11. Watson, S.D. & Lomas, K.J. & Buswell, R.A., 2019. "Decarbonising domestic heating: What is the peak GB demand?," Energy Policy, Elsevier, vol. 126(C), pages 533-544.
    12. Hamilton, Ian G. & Steadman, Philip J. & Bruhns, Harry & Summerfield, Alex J. & Lowe, Robert, 2013. "Energy efficiency in the British housing stock: Energy demand and the Homes Energy Efficiency Database," Energy Policy, Elsevier, vol. 60(C), pages 462-480.
    13. Stankovic, L. & Stankovic, V. & Liao, J. & Wilson, C., 2016. "Measuring the energy intensity of domestic activities from smart meter data," Applied Energy, Elsevier, vol. 183(C), pages 1565-1580.
    14. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    15. Jenny Crawley & Despina Manouseli & Peter Mallaburn & Cliff Elwell, 2022. "An Empirical Energy Demand Flexibility Metric for Residential Properties," Energies, MDPI, vol. 15(14), pages 1-18, July.
    16. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    17. Semple, Sally & Jenkins, David, 2020. "Variation of energy performance certificate assessments in the European Union," Energy Policy, Elsevier, vol. 137(C).
    18. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
    19. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    20. Räsänen, Teemu & Voukantsis, Dimitrios & Niska, Harri & Karatzas, Kostas & Kolehmainen, Mikko, 2010. "Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data," Applied Energy, Elsevier, vol. 87(11), pages 3538-3545, November.
    21. Summerfield, A.J. & Oreszczyn, T. & Palmer, J. & Hamilton, I.G. & Li, F.G.N. & Crawley, J. & Lowe, R.J., 2019. "What do empirical findings reveal about modelled energy demand and energy ratings? Comparisons of gas consumption across the English residential sector," Energy Policy, Elsevier, vol. 129(C), pages 997-1007.
    22. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    23. Torriti, Jacopo, 2020. "Temporal aggregation: Time use methodologies applied to residential electricity demand," Utilities Policy, Elsevier, vol. 64(C).
    24. Jenny Crawley & Phillip Biddulph & Paul J. Northrop & Jez Wingfield & Tadj Oreszczyn & Cliff Elwell, 2019. "Quantifying the Measurement Error on England and Wales EPC Ratings," Energies, MDPI, vol. 12(18), pages 1-19, September.
    25. Westermann, Paul & Deb, Chirag & Schlueter, Arno & Evins, Ralph, 2020. "Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data," Applied Energy, Elsevier, vol. 264(C).
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