Adaptive data decomposition based quantile-long-short-term memory probabilistic forecasting framework for power demand side management of energy system. (March 2023)

• Record Type: Journal Article
• Authors: Yang, Wei; Jia, Li; Xu, Yue
• Is Part Of: Computers & electrical engineering. Volume 106(2023)
• Publication Date: 2023-03
• Subjects: Energy storage system -- Power demand side management -- Peak cutting and valley filling -- Data decomposition -- Quantile long-short-term memory -- Probability load prediction; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2023.108584
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Load management can improve the overall benefit of power system through peak cutting and valley filling, whose performance depends on the accuracy of load forecasting. However, how to efficiently utilize load data to uprate forecasting performance is urgent. Therefore, a peak cutting and valley filling forecasting algorithm based on load operation state is proposed. Firstly, adaptive data decomposition based quantile-long-short-term memory (QLSTM) probabilistic forecasting framework is proposed to reflect the future load information more comprehensively. The method combines long-short-term memory (LSTM) with pinball loss function to provide deterministic forecasting and probability density forecasting. Then, a variable power peak cutting and valley filling algorithm is proposed to suppress the peak–valley difference and optimize the power structure. Finally, the case study of typical working conditions of actual power grid is performed to confirm that the proposed method can better reflect the future load information and reduce peak–valley difference. Graphical abstract: Highlights: A novel, non-parametric, probabilistic load forecasting method is presented. Training time is reduced due to obtaining multiple quantile outputs in one training. The proposed adaptive TVF-EMD algorithm can improve the load forecasting performance. A variable power peak cutting and valley filling algorithm is proposed.