(1.国网四川省电力公司计量中心，四川 成都，610045;2.四川大学电气工程学院，四川 成都，610065)
张 姝（1988—），女，博士，助理研究员，主要从事负荷建模与调控研究；E?mail: ZS20061621@163.com
(1.Metering Center, State Grid Sichuan Electric Power Company, Chengdu 610065, China;2.College of Electrical Engineering, Sichuan University, Chengdu 610065, China)
Non?intrusive load disaggregation technology can effectively mine the appliance information of customers, which is the basis to carry out interactive customer load response by the grid company. The conventional non?intrusive load disaggregation technology has several drawbacks, such as limited scope of application and low accuracy. In this paper, a non?intrusive load disaggregation model with multiple optimization selection of appliance characteristics is proposed. First, an adaptive sliding data window is designed for appliance operation characteristics to obtain a more complete power segment and to adjust the network input and output dimensions. Second, the appliance features can be extracted and deepened by fusing shallow convolutional neural networks (CNN) with two?layer nested long and short?term memory networks (NLSTM), which is further fed into an improved attention mechanism to obtain the optimum appliance feature sequence by adjusting the feature weights. Finally, experimental analysis on the REDD dataset shows that the multiple selection, deepening and reusing of appliance features can significantly improve the accuracy of load decomposition while reducing training time.
王家驹,王竣平,白 泰,张 然,丁熠辉,杨 林,张 姝.基于设备特征多层优选和CNN‑NLSTM模型的非侵入式负荷分解[J].电力科学与技术学报,2023,(1):146-153. WANG Jiaju, WANG Junping, BAI Tai, ZHANG Ran, DING Yihui, YANG Lin, ZHANG Shu. Non‑intrusive load disaggregation based on multiple optimization of appliance features and CNN‑NLSTM model[J]. Journal of Electric Power Science and Technology,2023,(1):146-153.复制