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Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

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  • Angreine Kewo

    (DTU Management, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
    Informatics Engineering Department, De La Salle Catholic University, Manado 95253, Indonesia)

  • Pinrolinvic D. K. Manembu

    (Electrical Engineering Department, Sam Ratulangi University, Manado 95115, Indonesia)

  • Per Sieverts Nielsen

    (DTU Management, Technical University of Denmark, 2800 Kongens Lyngby, Denmark)

Abstract

It is important to understand residential energy use as it is a large energy consumption sector and the potential for change is of great importance for global energy sustainability. A large energy-saving potential and emission reduction potential can be achieved, among others, by understanding energy consumption patterns in more detail. However, existing studies show that it requires many input parameters or disaggregated individual end-uses input data to generate the load profiles. Therefore, we have developed a simplified approach, called weighted proportion (Wepro) model, to synthesise the residential electricity load profile by proportionally matching the city’s main characteristics: Age group, labour force and gender structure with the representative households profiles provided in the load profile generator. The findings indicate that the synthetic load profiles can represent the local electricity consumption characteristics in the case city of Amsterdam based on time variation analyses. The approach is in particular advantageous to tackle the drawbacks of the existing studies and the standard load model used by the utilities. Furthermore, the model is found to be more efficient in the computational process of the residential sector’s load profiles, given the number of households in the city that is represented in the local profile.

Suggested Citation

  • Angreine Kewo & Pinrolinvic D. K. Manembu & Per Sieverts Nielsen, 2020. "Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model," Energies, MDPI, vol. 13(14), pages 1-28, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3543-:d:382554
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    1. Pablo-Romero, María del P. & Pozo-Barajas, Rafael & Yñiguez, Rocío, 2017. "Global changes in residential energy consumption," Energy Policy, Elsevier, vol. 101(C), pages 342-352.
    2. Ming Meng & Dongxiao Niu & Wei Sun, 2011. "Forecasting Monthly Electric Energy Consumption Using Feature Extraction," Energies, MDPI, vol. 4(10), pages 1-13, September.
    3. Eggimann, Sven & Hall, Jim W. & Eyre, Nick, 2019. "A high-resolution spatio-temporal energy demand simulation to explore the potential of heating demand side management with large-scale heat pump diffusion," Applied Energy, Elsevier, vol. 236(C), pages 997-1010.
    4. Do, Linh Phuong Catherine & Lin, Kuan-Heng & Molnár, Peter, 2016. "Electricity consumption modelling: A case of Germany," Economic Modelling, Elsevier, vol. 55(C), pages 92-101.
    5. Kobus, Charlotte B.A. & Klaassen, Elke A.M. & Mugge, Ruth & Schoormans, Jan P.L., 2015. "A real-life assessment on the effect of smart appliances for shifting households’ electricity demand," Applied Energy, Elsevier, vol. 147(C), pages 335-343.
    6. 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.
    7. Daioglou, Vassilis & van Ruijven, Bas J. & van Vuuren, Detlef P., 2012. "Model projections for household energy use in developing countries," Energy, Elsevier, vol. 37(1), pages 601-615.
    8. Ahn, Young-Hwan & Woo, Jung-Hun & Wagner, Fabian & Yoo, Seung Jick, 2019. "Downscaled energy demand projection at the local level using the Iterative Proportional Fitting procedure," Applied Energy, Elsevier, vol. 238(C), pages 384-400.
    9. Andersen, F.M. & Larsen, H.V. & Gaardestrup, R.B., 2013. "Long term forecasting of hourly electricity consumption in local areas in Denmark," Applied Energy, Elsevier, vol. 110(C), pages 147-162.
    10. Kipping, A. & Trømborg, E., 2015. "Hourly electricity consumption in Norwegian households – Assessing the impacts of different heating systems," Energy, Elsevier, vol. 93(P1), pages 655-671.
    11. Wai-Ming To & Peter Ka Chun Lee & Tsz-Ming Lai, 2017. "Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong," Energies, MDPI, vol. 10(7), pages 1-16, June.
    12. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    13. Beck, T. & Kondziella, H. & Huard, G. & Bruckner, T., 2016. "Assessing the influence of the temporal resolution of electrical load and PV generation profiles on self-consumption and sizing of PV-battery systems," Applied Energy, Elsevier, vol. 173(C), pages 331-342.
    14. Linssen, Jochen & Stenzel, Peter & Fleer, Johannes, 2017. "Techno-economic analysis of photovoltaic battery systems and the influence of different consumer load profiles," Applied Energy, Elsevier, vol. 185(P2), pages 2019-2025.
    15. Yang, Ting & Ren, Minglun & Zhou, Kaile, 2018. "Identifying household electricity consumption patterns: A case study of Kunshan, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 861-868.
    16. Andersen, Frits Møller & Baldini, Mattia & Hansen, Lars Gårn & Jensen, Carsten Lynge, 2017. "Households’ hourly electricity consumption and peak demand in Denmark," Applied Energy, Elsevier, vol. 208(C), pages 607-619.
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    1. Angreine Kewo & Pinrolinvic D. K. Manembu & Per Sieverts Nielsen, 2023. "A Rigorous Standalone Literature Review of Residential Electricity Load Profiles," Energies, MDPI, vol. 16(10), pages 1-27, May.

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