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Time-Use Data Modelling of Domestic, Commercial and Industrial Electricity Demand for the Scottish Islands

Author

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  • Chris Matthew

    (Energy Institute, Bartlett School of Environment, Energy and Resources, University College London, London WC1H 0NN, UK)

  • Catalina Spataru

    (Energy Institute, Bartlett School of Environment, Energy and Resources, University College London, London WC1H 0NN, UK)

Abstract

Achieving emissions reduction targets requires improved energy efficiency to avoid an oversized and excessively expensive electricity network. This can be analysed using hourly demand modelling that captures behaviour profiles, technology types, weather factors and building typologies. Numerous domestic models exist, but whole systems energy modelling, including commercial and industrial demand, are limited by data availability. Time-use survey data has typically been used to model domestic demand- in this work is expanded to also model commercial and industrial electricity-heating for the Scottish islands at an hourly and individual building level. This method is widely applicable for modelling whole system energy demand wherever time-use survey data are available. Combinatorial optimisation has been applied to generate a synthetic population, match individuals to properties and apply construction types to building polygons. SimStock is used for heating and lighting modelling. Validation of the model with 2016 data shows that it reflects longer term trends, with a monthly mean absolute percentage error (MAPE) of 1.6% and an R 2 of 0.99. At the hourly level, the MAPE of 6.2% and R 2 of 0.87 show the model captures variability needed to combine it with a supply-side model. Dataset accuracy, variability in the date recorded, missing data and unknown data correlations are discussed as causes for error. The model can be adapted for other regions and used to analyse the costs and benefits of energy efficiency measures with a supply-side generation model.

Suggested Citation

  • Chris Matthew & Catalina Spataru, 2023. "Time-Use Data Modelling of Domestic, Commercial and Industrial Electricity Demand for the Scottish Islands," Energies, MDPI, vol. 16(13), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5057-:d:1183069
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    References listed on IDEAS

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    1. 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.
    2. Druckman, A. & Jackson, T., 2008. "Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model," Energy Policy, Elsevier, vol. 36(8), pages 3167-3182, August.
    3. Ahmed Gassar, Abdo Abdullah & Yun, Geun Young & Kim, Sumin, 2019. "Data-driven approach to prediction of residential energy consumption at urban scales in London," Energy, Elsevier, vol. 187(C).
    4. Anderson, Ben & Torriti, Jacopo, 2018. "Explaining shifts in UK electricity demand using time use data from 1974 to 2014," Energy Policy, Elsevier, vol. 123(C), pages 544-557.
    5. Lorimer, Stephen, 2012. "A housing stock model of non-heating end-use energy in England verified by aggregate energy use data," Energy Policy, Elsevier, vol. 50(C), pages 419-427.
    6. Adeoye, Omotola & Spataru, Catalina, 2019. "Modelling and forecasting hourly electricity demand in West African countries," Applied Energy, Elsevier, vol. 242(C), pages 311-333.
    7. Torriti, Jacopo, 2020. "Temporal aggregation: Time use methodologies applied to residential electricity demand," Utilities Policy, Elsevier, vol. 64(C).
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    Cited by:

    1. Matthew, Chris, 2024. "The multiple benefits of current and potential energy efficiency policies: A Scottish islands case study," Energy Policy, Elsevier, vol. 187(C).

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