Power load identification based on Long-and-Short-Term Memory network and Affinity Propagation clustering algorithm. (July 2022)

• Record Type: Journal Article
• Authors: Liu, Biqi; Zhang, Fuxiang; Li, Xi; Wu, He; Xu, Libo
• Is Part Of: Energy reports. Volume 8(2022)Supplement 4
• Page Start: 1137
• Page End: 1144
• Publication Date: 2022-07
• Subjects: AP clustering algorithm -- LSTM -- Non-intrusive load detection; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.02.037
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Load monitoring and decomposition have broad application prospects in the field of smart grid. Based on non-invasive load monitoring technology, a power load identification method based on Long-and-Short-Term Memory (LSTM) and Affinity Propagation (AP) clustering algorithm is proposed. The original power load data is processed by AP clustering algorithm, and a large number of measured sample data are divided into multiple clusters. The continuous variable state equipment load is discretized to obtain the power set of the sample equipment. The LSTM network model of load classification is established, input the processed data into the network model, and the optimal working state sequence and corresponding equipment power under the influence of error are obtained. The results obtained by LSTM algorithm alone and the predicted data obtained by combining LSTM algorithm with AP algorithm are compared with the actual load data, and the superiority of the algorithm is judged according to the degree of difference. The results show that the deviation between the two algorithms and the actual data is about 2%–7%, which is much lower than the deviation when LSTM algorithm is used alone.