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

被引：8
作者：
Poggi, Aurora [1 ]
Di Persio, Luca [1 ]
Ehrhardt, Matthias [2 ]
机构：
[1] Univ Verona, Coll Math, Dept Comp Sci, I-37134 Verona, Italy
[2] Univ Wuppertal, Chair Appl & Computat Math, D-42119 Wuppertal, Germany
来源：
APPLIEDMATH | 2023年 / 3卷 / 02期
关键词：
electricity price forecasting; univariate model; statistical method; autoregressive; machine learning; deep learning; neural network;
D O I：
10.3390/appliedmath3020018
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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.
引用
收藏
页码：316 / 342
页数：27
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