Electricity Day-Ahead Market Conditions and Their Effect on the Different Supervised Algorithms for Market Price Forecasting

Abstract

Participants in deregulated electricity markets face risks from price volatility due to various factors, including fuel prices, renewable energy production, electricity demand, and crises such as COVID-19 and energy-related issues. Price forecasting is used to mitigate risk in markets trading goods which have high price volatility. Forecasting in electricity markets is difficult and challenging as volatility is attributed to many unpredictable factors. This work studies and reports the performance both in terms of forecasting error and of computational time of forecasting algorithms that are based on Extreme Learning Machine, Artificial Neural Network, XGBoost and random forest. All these machine learning techniques are combined with the Bootstrap technique of creating new samples from the available ones in order to improve the forecasting errors. In order to assess the performance of these methodologies, the Day-Ahead market prices are divided into three classes, namely normal, extremely high and negative, and these algorithms are subsequently used to provide forecasts for the whole year 2020 of the German and Finnish Day-Ahead markets. The average yearly forecasting errors along with the computation time required by each methodology are reported. The findings indicate that the random forest algorithm performs best for the normal and extremely high price categories, while XGBoost demonstrates better results for the negative price category. The methodology based on Extreme Learning Machine requires the least computational time and achieves forecasting errors that are comparable to the best-performing methods.