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

Guoqiang Sun, Yang Zhao, Jianglong Li, Junfu Cui and Xiaoyan Qi

PLOS ONE, 2026, vol. 21, issue 6, 1-22

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.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0350071

DOI: 10.1371/journal.pone.0350071

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