A comparative analysis of single learning and ensemble learning approaches for the forecasting of electricity prices

Abstract

Forecasting electricity prices accurately is imperative in deregulated power markets. Nonetheless, the intricate nature of electricity prices, characterized by high frequency and volatility, poses a challenge in building an effective forecasting model for policymakers and scientists. Precision in the electricity price prediction is crucial for providing valuable guidance to market participants, helping them maximize their benefits. In prior studies, various methods, including statistical models and artificial neural network models, have been used to forecast electricity prices. This study proposes three ensemble learning approaches - AdaBoost-LSTM, AdaBoost-BLSTM, and AdaBoost-GRU - which combine the AdaBoost algorithm with LSTM, Bi-LSTM, and GRU networks, respectively, to enhance the accuracy of electricity price predictions. The study aims to assess the effectiveness of the proposed models by comparing their predictive performance with that of single RNN-based models (LSTM, Bi-LSTM, GRU) using daily maximum electricity prices from 2004 to 2008. Notably, there has been no existing research that compares the effectiveness of these single and hybrid models. In the current literature, these single models are widely acknowledged as potent tools for improving forecasting accuracy. On the other hand, although the proposed ensemble learning approaches obtained using the AdaBoost boosting technique have been used in areas such as financial forecasting so far, they have never been used in electricity price forecasting. Accuracy assessment utilizing R-squared and MAPE clearly demonstrates that the AdaBoost-BLSTM approach performs very closely to, but better than, other boosting ensemble approaches, and significantly better than single models.