DG-LSTM-SA model: A deep gated LSTM network with self-attention mechanism for power generation and load forecasting

Abstract

Accurate forecasting of power generation and load demand is essential for the reliable operation of modern energy systems. Traditional recurrent neural networks (RNNs) often struggle to capture long-term dependencies in complex power time series, whereas recent Transformer-based models can introduce substantial computational overhead. To address these limitations, we propose a Deep Gated Long Short-Term Memory network with Self-Attention (DG-LSTM-SA). The proposed model combines a multi-layer gated architecture with hierarchically embedded self-attention modules, enabling it to adaptively emphasize informative time steps and capture complex temporal patterns without a prohibitive increase in parameters. We evaluated DG-LSTM-SA on three real-world energy datasets (NEPOOL, Yichang, and Solar-Energy). The results demonstrate that DG-LSTM-SA consistently outperforms ten baseline models. Compared with standard RNN variants such as LSTM and GRU, DG-LSTM-SA substantially reduces forecasting errors, decreasing Mean Absolute Error by more than 75%. Furthermore, relative to state-of-the-art attention-based models (e.g., Informer and Crossformer), DG-LSTM-SA achieves competitive accuracy while maintaining a distinct advantage in computational efficiency and training speed. Comprehensive ablation studies further confirm that the proposed design is robust, accurate, and practical for real-world grid dispatch and operational decision-making.