Electricity Load and Price Forecasting Using a Hybrid Method Based Bidirectional Long Short-Term Memory with Attention Mechanism Model

Abstract

The accuracy of forecasting short- or medium-term electricity loads and prices is critical in the energy market. The amplitude and duration of abnormally high prices and load spikes can be detrimental to retailers and production systems. Therefore, predicting these spikes to effectively manage risk is critical. In this paper, a novel hybrid method that combines ensemble empirical mode decomposition (EEMD) algorithm and a bidirectional long short-term memory with attention mechanism (BiLSTM-AM) model is proposed to predict electricity loads and prices. A simple approach is proposed to determine the number of intrinsic mode functions (IMFs) that decompose raw data using EEMD to avoid overdecomposition, irrelevant components, and high computational cost. Each selected mode is then modeled with BiLSTM-AM to obtain a predicted sequence. These sequences are summed and then reverted to obtain the final predicted value. The proposed method is validated using two datasets (PMJ and Australian Energy Market Operator) with different time intervals to demonstrate the generality and robustness of the forecasts, especially in temporal valley or peak forecasting. The results show that the proposed method outperforms other methods in prediction accuracy and spike-capturing ability, with EEMD reducing the mean absolute percentage error (MAPE) by 53%, 54%, and 60%, respectively. In the three forecast periods, the average MAPE and R̲2 are 0.097 and 0.92, respectively. Furthermore, we use Kolmogorov-Smirnov predictive accuracy (KSPA) test and model confidence set (MCS) test to validate the superiority of the proposed model. The results demonstrate its suitability, reliability, and performance in short- and medium-term forecasting.