Deep learning time pattern attention mechanism-based short-term load forecasting method

Abstract

Accurate load forecasting is crucial to improve the stability and cost-efficiency of smart grid operations. However, how to integrate multiple significant factors for enhancing load forecasting performance is insufficiently investigated in previous studies. To fill the gap, this study proposes a novel hybrid deep learning model for short-term load forecasting. First, the long short-term memory network is utilized to capture patterns from historical load data. Second, a time pattern attention (TPA) mechanism is incorporated to improve feature extraction and learning capabilities. By discerning valuable features and eliminating irrelevant ones, the TPA mechanism enhances the learning process. Third, fully-connected layers are employed to integrate external factors such as climatic conditions, economic indicators, and temporal aspects. This comprehensive approach facilitates a deeper understanding of the impact of these factors on load profiles, leading to the development of a highly accurate load forecasting model. Rigorous experimental evaluations demonstrate the superior performance of the proposed approach in comparison to existing state-of-the-art load forecasting methodologies.

BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

“…an advanced LSTM-based dual-attention model, meticulously considering the myriad of influencing factors and the effects of time nodes on STLF. Liao et al [33], with their innovative fusion of LSTM and a time pattern attention mechanism, augmented STLF methodologies, emphasizing feature extraction and model versatility. By incorporating external factors, their comprehensive approach improved feature extraction and demonstrated superior performance compared to existing methods.…”