Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/244364 
Year of Publication: 
2021
Series/Report no.: 
MAGKS Joint Discussion Paper Series in Economics No. 35-2021
Publisher: 
Philipps-University Marburg, School of Business and Economics, Marburg
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.
Subjects: 
machine learning
electricity consumption forecast
arti cial neural network
time series forecast
JEL: 
C45
C53
Q47
Document Type: 
Working Paper

Files in This Item:
File
Size
792.87 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.