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Electricity Price Forecasting via Statistical and Deep Learning Approaches: The German Case

Authors :
Aurora Poggi
Luca Di Persio
Matthias Ehrhardt
Source :
AppliedMath, Vol 3, Iss 2, Pp 316-342 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Our research involves analyzing the latest models used for electricity price forecasting, which include both traditional inferential statistical methods and newer deep learning techniques. Through our analysis of historical data and the use of multiple weekday dummies, we have proposed an innovative solution for forecasting electricity spot prices. This solution involves breaking down the spot price series into two components: a seasonal trend component and a stochastic component. By utilizing this approach, we are able to provide highly accurate predictions for all considered time frames.

Details

Language :
English
ISSN :
26739909
Volume :
3
Issue :
2
Database :
Directory of Open Access Journals
Journal :
AppliedMath
Publication Type :
Academic Journal
Accession number :
edsdoj.353422e36dae484c9ca9913c1e864085
Document Type :
article
Full Text :
https://doi.org/10.3390/appliedmath3020018