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

Frontiers in Energy Research

Energy Research

Junhua Zhao¹  Jiaqi Ruan¹  Wei Liao²  Ruoyu Wang²  Yinghua Xie²  Qingwei Wang²  Jing Li² 

[1] School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China;Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China;Shenzhen Power Supply Co., Ltd., Shenzhen, China;

关键词: load forecasting;    deep learning;    time pattern attention;    smart grid;    data driven;   

DOI  :  10.3389/fenrg.2023.1227979

 received in 2023-05-24, accepted in 2023-07-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Liao, Ruan, Xie, Wang, Li, Wang and Zhao.