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

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.