Residential load forecasting based on LSTM fusing self-attention mechanism with pooling. (15th August 2021)

• Record Type: Journal Article
• Authors: Zang, Haixiang; Xu, Ruiqi; Cheng, Lilin; Ding, Tao; Liu, Ling; Wei, Zhinong; Sun, Guoqiang
• Is Part Of: Energy. Volume 229(2021)
• Publication Date: 2021-08-15
• Subjects: Residential load forecasting -- Interconnected users -- Pooling -- Self-attention mechanism; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.120682
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Day-ahead residential load forecasting is crucial for electricity dispatch and demand response in power systems. Electrical loads are characterized by volatility and uncertainty caused by external factors, especially for individual residential loads. With the deployment of advanced metering infrastructure, the acquisition of electricity consumptions of multiple residential customers is available. This paper proposes a novel day-ahead residential load forecasting method based on feature engineering, pooling, and a hybrid deep learning model. Feature engineering is performed using two-stage preprocessing on data from each user, i.e., decomposition and multi-source input dimension reconstruction. Pooling is then adopted to merge data from both the target user and its interconnected users, in a descending order based on mutual information. Finally, a hybrid model with two input channels is developed by combining long short-term memory (LSTM) with self-attention mechanism (SAM). The case studies are conducted on a practical dataset containing multiple residential users. Performance of the proposed load forecasting method using data pools of different groups of users as well as different input forms is compared. The effectiveness of input dimension reconstruction and hybrid model is also validated. The overall results demonstrate the superiority of the proposed load forecasting method through comparison with other benchmark methods. Highlights: The multi-source input is reconstructed to promote the feature extraction for model. LSTM-SAM model is proposed to improve the accuracy of residential load forecast. Pooling strategy is adopted to construct training samples for forecasting models. Optimal data pool and input form are investigated for better prediction performance.