Short-term electricity price forecasting based on similarity day screening, two-layer decomposition technique and Bi-LSTM neural network

Electricity price forecasting (EPF) has been challenged by the widespread grid integration of renewable energy (RE), so it is critical to develop a highly accurate and reliable EPF model. In this study, novel considerations of RE generation factors are made, and a quantitative model for the impact of RE on electricity prices is built using random forests (RF) and improved Mahalanobis Distance (IMD). To reduce data duplication, similar days of EPF are first selected. Then it is suggested to decompose the electricity price series into multiple intrinsic mode functions (IMF) and residuals with different frequencies using a two-layer decomposition model based on improved comprehensive ensemble empirical mode decomposition (ICEEMD) and variational mode decomposition (VMD), in order to reduce data noise and volatility. Finally, EPF model based on Bi-directional long short-term memory (Bi-LSTM) is established to forecast multiple subsequences, and the final price forecasting result is obtained after integrated processing. Experimental results show that the RF-IMD-ICEEMD-VMD-Bi-LSTM hybrid model can significantly improve the forecasting performance, reduce the prediction error of EPF, and has the best performance among the comparison models.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

In this study, a reliable and accurate electricity price forecasting model is developed, considering the impact of renewable energy on electricity prices. The model utilizes random forests and improved Mahalanobis Distance for building, and applies decomposition and processing techniques to reduce data noise and volatility. Experimental results indicate that the proposed hybrid model outperforms other comparison models in terms of forecasting performance.