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

Erik Heilmann (), Janosch Henze and Heike Wetzel
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Erik Heilmann: University of Kassel
Janosch Henze: University of Kassel

MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)

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.

Keywords: machine learning; electricity consumption forecast; artificial neural network; time series forecast (search for similar items in EconPapers)
JEL-codes: C45 C53 Q47 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-for
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Citations: View citations in EconPapers (2)

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