Effective electricity load forecasting using enhanced double-reservoir echo state network

Accurate electricity load prediction is essential to ensure the efficient, reliable, and secure operation of the power system. In this study, a hybrid forecasting method called improved backtracking search optimization algorithm (IBSA)–double-reservoir echo state network (DRESN) (IBSA–DRESN) is proposed on the basis of IBSA and DRESN. Mutual information is utilized to eliminate low-significance input features and retain key input features. The DRESN structure aims to increase the diversity of the network. Roulette strategy, adaptive mutation operator, and niche operator is introduced to improve the standard BSA algorithm. The IBSA is applied to optimize several critical parameters in the DRESN neural network. The proposed IBSA–DRESN method is evaluated using two electricity load datasets, namely, North-America and PJM. Compared with eight popular benchmark models, prediction results show that IBSA–DRESN is more accurate for one-step ahead electricity load forecasting. In one-day ahead forecasting, IBSA–DRESN obtains better prediction performance in most cases.

Authors

• Lu Peng
• Sheng-Xiang Lv
• Lin Wang
• Zi-Yun Wang

Keywords

• Electricity load forecasting
• Echo state network
• Mutual information
• Backtracking search optimization algorithm