标题：WRL: A combined model for short-term load forecasting
作者：Liu, Yuecan ;Zhang, Kun ;Zhen, Shuai ;Guan, Yongming ;Shi, Yuliang
作者机构：[Liu, Yuecan ;Zhang, Kun ;Shi, Yuliang ] School of Software, Shandong University, Jinan, China;[Zhang, Kun ;Zhen, Shuai ;Guan, Yongming ;Shi, Yuliang 更多
会议名称：3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019
会议日期：1 August 2019 through 3 August 2019
来源：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
关键词：Adjustment factors; Bi-LSTM networks; Load forecasting; RBF neural networks; Wavelet Decomposition
摘要：Load forecasting plays a vital role in economic construction and national security. The accuracy of short-term load forecasting will directly affect the quality of power supply and user experience, and will indirectly affect the stability and safety of the power system operation. In this paper, we present a novel short-term load forecasting model, which combines influencing factors analysis, Wavelet Decomposition feature extraction, Radial Basis Function (RBF) neural networks and Bidirectional Long Short-Term Memory (Bi-LSTM) networks (WRL below). The model uses wavelet decomposition to extract the main features of load data, analyzes its correlation with influencing factors, and then constructs corresponding adjustment factors. The RBF neural networks are used to forecast the feature subsequence related to external factors. Other subsequences are input into Bidirectional LSTM networks to forecast future values. Finally, the forecasting results are obtained by wavelet inverse transform. Experiments show that the proposed short-term load forecasting method is effective and feasible. © 2019, Springer Nature Switzerland AG.