Forecasting Electricity Prices Three Days in Advance: Comparison Between Multilayer Perceptron and Support Vector Machine Networks

• Published in: Energies (Basel) Vol. 18; no. 17; p. 4744
• Main Authors: Borkowski, Dariusz, Jaśkiewicz, Michał
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2025
• Subjects: Alternative energy sources; Analysis; Artificial intelligence; Automation; electricity price forecasting; Energy industry; multilayer perceptron; Neural networks; Polish electricity market; short-term forecast; support vector machine; Poland
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en18174744

Electricity prices are subject to constant changes, mainly owing to the increasing share of unstable renewable energy sources. The ability to predict short-term prices presents significant benefits to both energy consumers and producers. This is crucial for managing the energy in hybrid systems with energy storage. This study presents a methodology for predicting the electricity prices for three days with hourly resolution. The accuracy of the price prediction strongly depends on the stability and repeatability of the analysed energy market. The Polish market, characterised by a dynamically changing energy mix, where the selection of the training period and the training, validation, and test sets are crucial, is assessed. Two periods are analysed: 2019–2021, which is a period of stable prices, and 2022–2024, which is a period of high price variability. The multilayer perceptron (MLP) network and support vector machine (SVM) are trained using three sets of data: time, weather, and prices of various energy sources. The analysis indicates the correlation of data and their impact on the accuracy of the price forecast. Dedicated data processing, network model structures, and training techniques are used. The comparison between prediction accuracies shows the advantages of the SVM network, whose prediction error is lower by 45% for the period of stable prices and by 20% for the period of variable prices when compared with the MLP network. The results indicate a significant increase in accuracy when various types of training data, such as weather or energy prices, are considered.