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Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts

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  • Lindberg, K.B.
  • Bakker, S.J.
  • Sartori, I.

Abstract

Long-term forecasts of the aggregate electric load profile are crucial for grid investment decisions and energy system planning. With current developments in energy efficiency of new and renovated buildings, and the coupling of heating and electricity demand through heat pumps, the long-term load forecast cannot be based on its historic pattern anymore. This paper presents part of an on-going work aimed at improving forecasts of the electric load profile on a national level, based on a bottom-up approach. The proposed methodology allows to account for energy efficiency measures of buildings and introduction of heat pumps on the aggregated electric load profile. Based on monitored data from over 100 non-residential buildings from all over Norway, with hourly resolution, this paper presents panel data regression models for heat load and electric specific load separately. This distinction is crucial since it allows to consider future energy efficiency measures and substitution of heating technologies. The data set is divided into 7 building types, with two variants: regular and energy efficient. The load is dependent on hour of the day, outer temperature and type of day, such as weekday and weekend. The resulting parameter estimates characterize the energy signature for each building type and variant, normalized per floor area unit (m2). Hence, it is possible to generate load profiles for typical days, weeks and years, and make aggregated load forecasts for a given area, needing only outdoor temperature and floor areas as additional data inputs.

Suggested Citation

  • Lindberg, K.B. & Bakker, S.J. & Sartori, I., 2019. "Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts," Utilities Policy, Elsevier, vol. 58(C), pages 63-88.
    • Handle: RePEc:eee:juipol:v:58:y:2019:i:c:p:63-88
    DOI: 10.1016/j.jup.2019.03.004
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    Cited by:

    1. Mateo Jesper & Felix Pag & Klaus Vajen & Ulrike Jordan, 2022. "Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modeling," Sustainability, MDPI, vol. 14(7), pages 1-32, March.
    2. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    3. Berg, Kjersti & Hernandez-Matheus, Alejandro & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Farahmand, Hossein, 2024. "Load configuration impact on energy community and distribution grid: Quantifying costs, emissions and grid exchange," Applied Energy, Elsevier, vol. 363(C).
    4. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    5. Hofmann, Matthias & Lindberg, Karen Byskov, 2024. "Evidence of households' demand flexibility in response to variable hourly electricity prices – Results from a comprehensive field experiment in Norway," Energy Policy, Elsevier, vol. 184(C).
    6. Castillo, Victhalia Zapata & Boer, Harmen-Sytze de & Muñoz, Raúl Maícas & Gernaat, David E.H.J. & Benders, René & van Vuuren, Detlef, 2022. "Future global electricity demand load curves," Energy, Elsevier, vol. 258(C).
    7. Hanne Kauko & Daniel Rohde & Armin Hafner, 2020. "Local Heating Networks with Waste Heat Utilization: Low or Medium Temperature Supply?," Energies, MDPI, vol. 13(4), pages 1-16, February.
    8. Guo, Jiacheng & Zhang, Peiwen & Wu, Di & Liu, Zhijian & Liu, Xuan & Zhang, Shicong & Yang, Xinyan & Ge, Hua, 2022. "Multi-objective optimization design and multi-attribute decision-making method of a distributed energy system based on nearly zero-energy community load forecasting," Energy, Elsevier, vol. 239(PC).
    9. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.
    10. Foslie, Sverre Stefanussen & Knudsen, Brage Rugstad & Korpås, Magnus, 2023. "Integrated design and operational optimization of energy systems in dairies," Energy, Elsevier, vol. 281(C).
    11. Askeland, Magnus & Backe, Stian & Bjarghov, Sigurd & Korpås, Magnus, 2021. "Helping end-users help each other: Coordinating development and operation of distributed resources through local power markets and grid tariffs," Energy Economics, Elsevier, vol. 94(C).
    12. Jing, Rui & Hua, Weiqi & Lin, Jian & Lin, Jianyi & Zhao, Yingru & Zhou, Yue & Wu, Jianzhong, 2022. "Cost-efficient decarbonization of local energy systems by whole-system based design optimization," Applied Energy, Elsevier, vol. 326(C).
    13. Backe, Stian & Kara, Güray & Tomasgard, Asgeir, 2020. "Comparing individual and coordinated demand response with dynamic and static power grid tariffs," Energy, Elsevier, vol. 201(C).
    14. Michael Wood & Emanuele Ogliari & Alfredo Nespoli & Travis Simpkins & Sonia Leva, 2023. "Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies," Forecasting, MDPI, vol. 5(1), pages 1-18, March.
    15. Krzysztof Zagrajek, 2021. "A Survey Data Approach for Determining the Probability Values of Vehicle-to-Grid Service Provision," Energies, MDPI, vol. 14(21), pages 1-38, November.
    16. Li, Kang & Duan, Pengfei & Cao, Xiaodong & Cheng, Yuanda & Zhao, Bingxu & Xue, Qingwen & Feng, Mengdan, 2024. "A multi-energy load forecasting method based on complementary ensemble empirical model decomposition and composite evaluation factor reconstruction," Applied Energy, Elsevier, vol. 365(C).
    17. Salman Siddiqui & Mark Barrett & John Macadam, 2021. "A High Resolution Spatiotemporal Urban Heat Load Model for GB," Energies, MDPI, vol. 14(14), pages 1-28, July.
    18. Palmer Real, Jaume & Møller, Jan Kloppenborg & Li, Rongling & Madsen, Henrik, 2022. "A data-driven framework for characterising building archetypes: A mixed effects modelling approach," Energy, Elsevier, vol. 254(PB).

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