Electricity Spot Prices Forecasting Based on Ensemble Learning

Efficient modeling and forecasting of electricity prices are essential in today's competitive electricity markets. However, price forecasting is not easy due to the specific features of the electricity price series. This study examines the performance of an ensemble-based technique for forecasting short-term electricity spot prices in the Italian electricity market (IPEX). To this end, the price time series is divided into deterministic and stochastic components. The deterministic component that includes long-term trends, annual and weekly seasonality, and bank holidays, is estimated using semi-parametric techniques. On the other hand, the stochastic component considers the short-term dynamics of the price series and is estimated by time series and various machine learning algorithms. Based on three standard accuracy measures, the results indicate that the ensemble-based model outperforms the others, while the random forest and ARMA are highly competitive.

Automated summary

Efficient modeling and forecasting of electricity prices are crucial in competitive electricity markets. This study examines the performance of an ensemble-based technique for short-term electricity spot price forecasting in the Italian electricity market. The results show that the ensemble-based model outperforms the others, while random forest and ARMA models are highly competitive.