Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/244364 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
MAGKS Joint Discussion Paper Series in Economics No. 35-2021
Verlag: 
Philipps-University Marburg, School of Business and Economics, Marburg
Zusammenfassung: 
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.
Schlagwörter: 
machine learning
electricity consumption forecast
arti cial neural network
time series forecast
JEL: 
C45
C53
Q47
Dokumentart: 
Working Paper

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