Day Ahead Electricity Price Forecasting with Neural Networks - One or Multiple Outputs?

被引：0

作者：

Kulinski, Wojciech ^{[1
]}

Sztyber-Betley, Anna ^{[1
]}

机构：

[1] Warsaw Univ Technol, Warsaw, Poland

来源：

DIGITAL INTERACTION AND MACHINE INTELLIGENCE, MIDI 2023 | 2024年 / 1076卷

关键词：

Neural networks; Deep learning; Electricity price forecasting; Energy market;

D O I：

10.1007/978-3-031-66594-3_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Electricity prices are an essential factor for industry and intelligent systems. An important part of energy trading takes place on the Day-ahead Market. Predicting prices in this market allows economically rational decisions to be made. Our study aims to model prices in the day-ahead market using neural networks. An attempt is made to reproduce the results of the paper [Marcjasz et al., 2020], where two network structures are compared: one with one output and one with 24 outputs. Our work adapts the modelling methodology to the current challenging market conditions and variable data reporting methods. We show that a structure predicting 24 values is more resilient in dynamic price conditions.

引用

页码：106 / 113

页数：8

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