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Machine learning in energy forecasts with an application to high frequency electricity consumption data

Author

Listed:
  • Erik Heilmann

    (University of Kassel)

  • Janosch Henze

    (University of Kassel)

  • Heike Wetzel

    (University of Kassel)

Abstract

Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing different model approaches and a standardized process of model selection. This paper provides a concise and comprehensible introduction to the topic by discussing the concept of machine learning in the context of energy economics and presenting an exemplary application to electricity load data. For this, we introduce and demonstrate the structured machine learning process containing the preparation, model selection and test of forecast models. This process is intended to serve as a general guideline for energy economists and practitioners who need to apply sophisticated forecast models.

Suggested Citation

  • Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    • Handle: RePEc:mar:magkse:202135
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    File URL: https://www.uni-marburg.de/en/fb02/research-groups/economics/macroeconomics/research/magks-joint-discussion-papers-in-economics/papers/2021-papers/35-2021_heilmann.pdf
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    References listed on IDEAS

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    Cited by:

    1. Heilmann, Erik, 2023. "The impact of transparency policies on local flexibility markets in electric distribution networks," Utilities Policy, Elsevier, vol. 83(C).
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    More about this item

    Keywords

    machine learning; electricity consumption forecast; artificial neural network; time series forecast;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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