电测与仪表2023，Vol.60Issue(12)：182-188,195,8.DOI:10.19753/j.issn1001-1390.2023.12.027
基于设备运行状态检测与能量回归同步评估的居民非介入式负荷辨识算法研究
Research on residential non-intrusive load identification algorithm based on equipment operation state detection and energy regression synchronous evaluation
摘要
Abstract
The non-intrusive load identification technology can efficiently and low-cost obtain the sub-item power of users and support a variety of services.The neural network based on the energy regression of sub item electrical appliances pro-vides an important support for the load identification technology.In this paper,aiming at the noise identification pollution at the equipment shutdown during the energy regression of the neural network and the limitations of the evaluation of e-quipment operation status based on the energy threshold method,a hard parameter sharing multi-task learning model based on the energy regression and status classification is proposed.According to the sensitivity difference between energy re-gression and status classification to the global and regional information of the input sequence,a time convolutional network based on multi-receptive field fusion is proposed.The experimental results show that the proposed DNN model has im-proved the disaggregation performance,and reduced the MAE by 50％compared with the traditional network on small power fluctuation devices such as washing machines and dishwashers.关键词
非侵入负荷辨识/多任务学习/多感受野融合/时间卷积Key words
NILM/multi-task learning/multi-receptive field fusion/time convolution分类
信息技术与安全科学引用本文复制引用
宋玮琼,王立永,宋威,朱肖晶,穆毅凡,冯燕钧..基于设备运行状态检测与能量回归同步评估的居民非介入式负荷辨识算法研究[J].电测与仪表,2023,60(12):182-188,195,8.基金项目
国家电网有限公司科技项目(SGBJDK00JLJS2250128) （SGBJDK00JLJS2250128）
