Attention Mechanism Multi-Size Depthwise Convolutional Long Short-Term Memory Neural Network for Forecasting Real-Time Electricity Prices
Huifeng Xu, Feihu Hu, Xinhao Liang, Mohammad Abu Gunmi
Abstract
Real-time electricity price forecasting affects both the interests of power companies and the stability of power systems. Although deep learning models have achieved rich results in forecasting, due to the variable temporal characteristics and numerous influencing factors of real-time electricity prices, it is difficult for general deep learning models to extract electricity price features with obvious regularity, which affects forecasting accuracy. To solve this problem, this paper proposes an attention mechanism multi-size depthwise convolutional long short-term memory neural network (AM-MDC-LSTM) for predicting real-time electricity prices. The model improves prediction capability in the following aspects. (1) Using an attention mechanism to adaptively assign weights to electricity price time series and electricity price exogenous variables (production, consumption, electricity prices in neighboring regions) to improve electricity price feature extraction efficiency. (2) Using convolution kernels of different sizes to convolve individual electricity price exogenous variables one by one to extract local burst and global periodic electricity price features with obvious regularity. This is then combined with long short-term memory networks to extract temporal features reflected in electricity prices. Experimental results conducted in the Nordic and PJM electricity markets demonstrate that the proposed model outperforms other models discussed in the paper, exhibiting higher prediction accuracy.