IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i8p2167-d535322.html
   My bibliography  Save this article

A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles

Author

Listed:
  • Marlon Schlemminger

    (Department Solar Energy, Leibniz University Hannover, Appelstr. 2, 30167 Hannover, Germany)

  • Raphael Niepelt

    (Department Solar Energy, Leibniz University Hannover, Appelstr. 2, 30167 Hannover, Germany
    Institute for Solar Energy Research Hamelin (ISFH), Am Ohrberg 1, 31860 Emmerthal, Germany)

  • Rolf Brendel

    (Department Solar Energy, Leibniz University Hannover, Appelstr. 2, 30167 Hannover, Germany
    Institute for Solar Energy Research Hamelin (ISFH), Am Ohrberg 1, 31860 Emmerthal, Germany)

Abstract

End-use specific residential electricity load profiles are of interest for energy system modelling that requires future load curves or demand-side management. We present a model that is applicable across countries to predict consumption on a regional and national scale, using openly available data. The model uses neural networks (NNs) to correlate measured consumption from one country (United Kingdom) with weather data and daily profiles of a mix of human activity and device specific power profiles. We then use region-specific weather data and time-use surveys as input for the trained NNs to predict unscaled electric load profiles. The total power profile consists of the end-use household load profiles scaled with real consumption. We compare the model’s results with measured and independently simulated profiles of various European countries. The NNs achieve a mean absolute error compared with the average load of 6.5 to 33% for the test set. For Germany, the standard deviation between the simulation, the standard load profile H0, and measurements from the University of Applied Sciences Berlin is 26.5%. Our approach reduces the amount of input data required compared with existing models for modelling region-specific electricity load profiles considering end-uses and seasonality based on weather parameters. Hourly load profiles for 29 European countries based on four historical weather years are distributed under an open license.

Suggested Citation

  • Marlon Schlemminger & Raphael Niepelt & Rolf Brendel, 2021. "A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles," Energies, MDPI, vol. 14(8), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2167-:d:535322
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/8/2167/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/8/2167/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stefan Arens & Karen Derendorf & Frank Schuldt & Karsten Von Maydell & Carsten Agert, 2018. "Effect of EV Movement Schedule and Machine Learning-Based Load Forecasting on Electricity Cost of a Single Household," Energies, MDPI, vol. 11(11), pages 1-19, October.
    2. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
    3. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    4. Bingtuan Gao & Xiaofeng Liu & Zhenyu Zhu, 2018. "A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents," Energies, MDPI, vol. 11(8), pages 1-16, August.
    5. Lombardi, Francesco & Balderrama, Sergio & Quoilin, Sylvain & Colombo, Emanuela, 2019. "Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model," Energy, Elsevier, vol. 177(C), pages 433-444.
    6. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    7. Martin Robinius & Felix ter Stein & Adrien Schwane & Detlef Stolten, 2017. "A Top-Down Spatially Resolved Electrical Load Model," Energies, MDPI, vol. 10(3), pages 1-16, March.
    8. Frondel, Manuel & Janßen-Timmen, Ronald & Sommer, Stephan, 2019. "Erstellung der Anwendungsbilanzen 2018 für den Sektor der Privaten Haushalte und den Verkehrssektor in Deutschland: Endbericht - August 2019," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 215903.
    9. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    10. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    11. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    12. Behm, Christian & Nolting, Lars & Praktiknjo, Aaron, 2020. "How to model European electricity load profiles using artificial neural networks," Applied Energy, Elsevier, vol. 277(C).
    13. Stephan Kolassa & Wolfgang Schütz, 2007. "Advantages of the MAD/Mean Ratio over the MAPE," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 40-43, Spring.
    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. Peacock, Malcolm & Fragaki, Aikaterini & Matuszewski, Bogdan J, 2023. "The impact of heat electrification on the seasonal and interannual electricity demand of Great Britain," Applied Energy, Elsevier, vol. 337(C).
    2. Lohr, C. & Schlemminger, M. & Peterssen, F. & Bensmann, A. & Niepelt, R. & Brendel, R. & Hanke-Rauschenbach, R., 2022. "Spatial concentration of renewables in energy system optimization models," Renewable Energy, Elsevier, vol. 198(C), pages 144-154.

    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. 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.
    2. 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.
    3. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    4. Amin, Amin & Mourshed, Monjur, 2024. "Community stochastic domestic electricity forecasting," Applied Energy, Elsevier, vol. 355(C).
    5. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.
    6. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
    7. Miguel López & Carlos Sans & Sergio Valero & Carolina Senabre, 2018. "Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting," Energies, MDPI, vol. 11(8), pages 1-19, August.
    8. Riva, Fabio & Gardumi, Francesco & Tognollo, Annalisa & Colombo, Emanuela, 2019. "Soft-linking energy demand and optimisation models for local long-term electricity planning: An application to rural India," Energy, Elsevier, vol. 166(C), pages 32-46.
    9. Lu Jiang & Xingpeng Chen & Bing Xue, 2019. "Features, Driving Forces and Transition of the Household Energy Consumption in China: A Review," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    10. 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.
    11. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    12. Taniguchi-Matsuoka, Ayako & Shimoda, Yoshiyuki & Sugiyama, Minami & Kurokawa, Yusuke & Matoba, Haruka & Yamasaki, Tomoya & Morikuni, Taro & Yamaguchi, Yohei, 2020. "Evaluating Japan’s national greenhouse gas reduction policy using a bottom-up residential end-use energy simulation model," Applied Energy, Elsevier, vol. 279(C).
    13. Ping Ma & Shuhui Cui & Mingshuai Chen & Shengzhe Zhou & Kai Wang, 2023. "Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System," Energies, MDPI, vol. 16(15), pages 1-17, August.
    14. 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.
    15. Huang, Yunyou & Zhan, Jianfeng & Luo, Chunjie & Wang, Lei & Wang, Nana & Zheng, Daoyi & Fan, Fanda & Ren, Rui, 2019. "An electricity consumption model for synthesizing scalable electricity load curves," Energy, Elsevier, vol. 169(C), pages 674-683.
    16. Zou, Rongwei & Yang, Qiliang & Xing, Jianchun & Zhou, Qizhen & Xie, Liqiang & Chen, Wenjie, 2024. "Predicting the electric power consumption of office buildings based on dynamic and static hybrid data analysis," Energy, Elsevier, vol. 290(C).
    17. 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).
    18. Luca Massidda & Marino Marrocu, 2018. "Smart Meter Forecasting from One Minute to One Year Horizons," Energies, MDPI, vol. 11(12), pages 1-16, December.
    19. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
    20. Hayn, Marian & Bertsch, Valentin & Zander, Anne & Nickel, Stefan & Fichtner, Wolf, 2016. "The impact of electricity tariffs on residential demand side flexibility," Working Paper Series in Production and Energy 14, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).

    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:14:y:2021:i:8:p:2167-:d:535322. 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.