[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i14p3543-d382554.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/14/3543/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/14/3543/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erik Dahlquist & Fredrik Wallin & Koteshwar Chirumalla & Reza Toorajipour & Glenn Johansson, 2023. "Balancing Power in Sweden Using Different Renewable Resources, Varying Prices, and Storages Like Batteries in a Resilient Energy System," Energies, MDPI, vol. 16(12), pages 1-28, June.
    2. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    3. Ramallo-González, Alfonso P. & Loonen, Roel & Tomat, Valentina & Zamora, Miguel Ángel & Surugin, Dmitry & Hensen, Jan, 2020. "Nomograms for de-complexing the dimensioning of off-grid PV systems," Renewable Energy, Elsevier, vol. 161(C), pages 162-172.
    4. Avilés A., Camilo & Oliva H., Sebastian & Watts, David, 2019. "Single-dwelling and community renewable microgrids: Optimal sizing and energy management for new business models," Applied Energy, Elsevier, vol. 254(C).
    5. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    6. Miguel Ángel Pardo & Héctor Fernández & Antonio Jodar-Abellan, 2020. "Converting a Water Pressurized Network in a Small Town into a Solar Power Water System," Energies, MDPI, vol. 13(15), pages 1-26, August.
    7. Luthander, Rasmus & Nilsson, Annica M. & Widén, Joakim & Åberg, Magnus, 2019. "Graphical analysis of photovoltaic generation and load matching in buildings: A novel way of studying self-consumption and self-sufficiency," Applied Energy, Elsevier, vol. 250(C), pages 748-759.
    8. Schopfer, S. & Tiefenbeck, V. & Staake, T., 2018. "Economic assessment of photovoltaic battery systems based on household load profiles," Applied Energy, Elsevier, vol. 223(C), pages 229-248.
    9. Palacios-Garcia, E.J. & Moreno-Munoz, A. & Santiago, I. & Flores-Arias, J.M. & Bellido-Outeirino, F.J. & Moreno-Garcia, I.M., 2018. "A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector," Energy, Elsevier, vol. 144(C), pages 1080-1091.
    10. Andersen, F.M. & Gunkel, P.A. & Jacobsen, H.K. & Kitzing, L., 2021. "Residential electricity consumption and household characteristics: An econometric analysis of Danish smart-meter data," Energy Economics, Elsevier, vol. 100(C).
    11. Koskela, Juha & Rautiainen, Antti & Järventausta, Pertti, 2019. "Using electrical energy storage in residential buildings – Sizing of battery and photovoltaic panels based on electricity cost optimization," Applied Energy, Elsevier, vol. 239(C), pages 1175-1189.
    12. Bruno Domenech & Gema Calleja & Jordi Olivella, 2021. "Residential Photovoltaic Profitability with Storage under the New Spanish Regulation: A Multi-Scenario Analysis," Energies, MDPI, vol. 14(7), pages 1-17, April.
    13. Azuatalam, Donald & Paridari, Kaveh & Ma, Yiju & Förstl, Markus & Chapman, Archie C. & Verbič, Gregor, 2019. "Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 555-570.
    14. Kipping, A. & Trømborg, E., 2017. "Modeling hourly consumption of electricity and district heat in non-residential buildings," Energy, Elsevier, vol. 123(C), pages 473-486.
    15. 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).
    16. Gudmunds, D. & Nyholm, E. & Taljegard, M. & Odenberger, M., 2020. "Self-consumption and self-sufficiency for household solar producers when introducing an electric vehicle," Renewable Energy, Elsevier, vol. 148(C), pages 1200-1215.
    17. Despeghel, Jolien & Tant, Jeroen & Driesen, Johan, 2024. "Convex optimization of PV-battery system sizing and operation with non-linear loss models," Applied Energy, Elsevier, vol. 353(PA).
    18. Alejandro Pena-Bello & Edward Barbour & Marta C. Gonzalez & Selin Yilmaz & Martin K. Patel & David Parra, 2020. "How Does the Electricity Demand Profile Impact the Attractiveness of PV-Coupled Battery Systems Combining Applications?," Energies, MDPI, vol. 13(15), pages 1-19, August.
    19. Aniello, Gianmarco & Shamon, Hawal & Kuckshinrichs, Wilhelm, 2021. "Micro-economic assessment of residential PV and battery systems: The underrated role of financial and fiscal aspects," Applied Energy, Elsevier, vol. 281(C).
    20. Marion R. Dam & Marten D. van der Laan, 2024. "Techno-Economic Assessment of Battery Systems for PV-Equipped Households with Dynamic Contracts: A Case Study of The Netherlands," Energies, MDPI, vol. 17(12), pages 1-24, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3543-:d:382554. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.