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Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels

Author

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  • Leonard Burg

    (School of Business and Economics, RWTH Aachen University, 52062 Aachen, Germany)

  • Gonca Gürses-Tran

    (E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany)

  • Reinhard Madlener

    (E.ON Energy Research Center, Institute for Future Energy Consumer Needs and Behavior, School of Business and Economics, RWTH Aachen University, 52074 Aachen, Germany
    Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU), Sentralbygg 1, Gløshaugen, 7491 Trondheim, Norway)

  • Antonello Monti

    (E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany)

Abstract

Power system operators are confronted with a multitude of new forecasting tasks to ensure a constant supply security despite the decreasing number of fully controllable energy producers. With this paper, we aim to facilitate the selection of suitable forecasting approaches for the load forecasting problem. First, we provide a classification of load forecasting cases in two dimensions: temporal and hierarchical. Then, we identify typical features and models for forecasting and compare their applicability in a structured manner depending on six previously defined cases. These models are compared against real data in terms of their computational effort and accuracy during development and testing. From this comparative analysis, we derive a generic guide for the selection of the best prediction models and features per case.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7128-:d:669709
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    References listed on IDEAS

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    1. Pape, Christian, 2018. "The impact of intraday markets on the market value of flexibility — Decomposing effects on profile and the imbalance costs," Energy Economics, Elsevier, vol. 76(C), pages 186-201.
    2. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    3. Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    4. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
    5. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    6. Seyed Mahdi Miraftabzadeh & Michela Longo & Federica Foiadelli & Marco Pasetti & Raul Igual, 2021. "Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey," Energies, MDPI, vol. 14(16), pages 1-24, August.
    7. Hirth, Lion, 2013. "The market value of variable renewables," Energy Economics, Elsevier, vol. 38(C), pages 218-236.
    8. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    9. Behm, Christian & Nolting, Lars & Praktiknjo, Aaron, 2020. "How to model European electricity load profiles using artificial neural networks," Applied Energy, Elsevier, vol. 277(C).
    10. Ibrahim Salem Jahan & Vaclav Snasel & Stanislav Misak, 2020. "Intelligent Systems for Power Load Forecasting: A Study Review," Energies, MDPI, vol. 13(22), pages 1-12, November.
    11. Ruhnau, Oliver & Hennig, Patrick & Madlener, Reinhard, 2020. "Economic implications of forecasting electricity generation from variable renewable energy sources," Renewable Energy, Elsevier, vol. 161(C), pages 1318-1327.
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    Cited by:

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    2. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.

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