Short-term electricity price forecasting through demand and renewable generation prediction

E. Belenguer, J. Segarra-Tamarit, Esteban Perez Caldentey and R. Vidal-Albalate

Mathematics and Computers in Simulation (MATCOM), 2025, vol. 229, issue C, 350-361

Abstract: Electricity market prices depend on various variables, including energy demand, weather conditions, gas prices, renewable generation, and other factors. Fluctuating prices are a common characteristic of electricity markets, making electricity price forecasting a complex process where predicting different variables is crucial. This paper introduces a hybrid forecasting model developed for the Spanish case. The model comprises four forecasting tools, with three of them relying on artificial neural networks, while the demand forecasting model employs a similar-day approach with temperature correction. This model can be employed by electrical energy trading companies to enhance their trading strategies in the day-ahead market and in derivative markets with a time horizon ranging from two to ten days. The results indicate that, with a forecasting horizon of two days, the price forecast has a rMAE of 8.18%. Furthermore, the model enables a market agent to accurately decide whether to purchase energy in the daily market or in the derivatives market in 69.9% of the days.

Keywords: Electricity price forecasting; Electricity markets; Machine learning; Renewable generation forecasting (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:229:y:2025:i:c:p:350-361

DOI: 10.1016/j.matcom.2024.10.004

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