Electricity Price Forecasting via Statistical and Deep Learning Approaches: The German Case-Reference-Cited by-同舟云学术

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

Published:2023-04-03 Issue:2 Volume:3 Page:316-342
ISSN:2673-9909
Container-title:AppliedMath
language:en
Short-container-title:AppliedMath

Author:

Poggi Aurora¹^ORCID,Di Persio Luca¹^ORCID,Ehrhardt Matthias²^ORCID

Affiliation:

1. Department of Computer Science, College of Mathematics, University of Verona, 37134 Verona, Italy

2. Chair of Applied and Computational Mathematics, University of Wuppertal, 42119 Wuppertal, Germany

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.

Publisher

MDPI AG

Link

https://www.mdpi.com/2673-9909/3/2/18/pdf

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