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Advanced price forecasting in agent-based electricity market simulation

Fraunholz, Christoph; Kraft, Emil ORCID iD icon; Keles, Dogan; Fichtner, Wolf

Machine learning and agent-based modeling are two popular tools in energy research. In this article, we propose an innovative methodology that combines these methods. For this purpose, we develop an electricity price forecasting technique using artificial neural networks and integrate the novel approach into the established agent-based electricity market simulation model PowerACE. In a case study covering ten interconnected European countries and a time horizon from 2020 until 2050 at hourly resolution, we benchmark the new forecasting approach against a simpler linear regression model as well as a naive forecast. Contrary to most of the related literature, we also evaluate the statistical significance of the superiority of one approach over another by conducting Diebold–Mariano hypothesis tests. Our major results can be summarized as follows. Firstly, in contrast to real-world electricity price forecasts, we find the naive approach to perform very poorly when deployed model-endogenously (mean absolute percentage error 0.40–0.53). Secondly, although the linear regression performs reasonably well (mean absolute percentage error 0.17–0.32), it is outperformed by the neural network approach (mean absolute percentage error 0.17–0.21). ... mehr

DOI: 10.1016/j.apenergy.2021.116688
Zitationen: 11
Zitationen: 12
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 05.2021
Sprache Englisch
Identifikator ISSN: 0306-2619
KITopen-ID: 1000130535
HGF-Programm 37.12.01 (POF IV, LK 01) Digitalization & System Technology for Flexibility Solutions
Erschienen in Applied energy
Verlag Elsevier
Band 290
Seiten 116688
Projektinformation ENSURE_IAI (BMBF, 03SFK1F0-2)
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