Machine Learning in Energy Forecasts with an Application to High Frequency Electricity Consumption Data

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 diff...

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Bibliographic Details
Published in:MAGKS - Joint Discussion Paper Series in Economics (Band 35-2021)
Main Authors: Heilmann, Erik, Henze, Janosch, Wetzel, Heike
Format: Article
Language:English
Published: Philipps-Universität Marburg 2021
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Online Access:PDF Full Text
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Summary: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.
Physical Description:29 Pages
ISSN:1867-3678
DOI:10.17192/es2024.0706