中国电力2025，Vol.58Issue(5)：33-42,10.DOI:10.11930/j.issn.1004-9649.202403063
融合SENet注意力机制和GA-CNN的非侵入式负荷识别方法
A Non-invasive Load Recognition Approach Incorporating SENet Attention Mechanism and GA-CNN
摘要
Abstract
With the popularization of smart meters and the gradual improvement of grid informatization and digitization,non-intrusive load monitoring(NILM)on the demand side of residential customers'energy usage is becoming one of the key technologies for power supply companies to boost energy efficiency.Regarding the problems of the current non-intrusive load recognition algorithms,such as feature redundancy,high computational overhead,and low recognition performance,the paper proposes a non-intrusive load recognition method integrating SENet attention mechanism and GA-CNN.Firstly,the SENet attention mechanism is embedded in a convolutional neural network(CNN)to improve the characterizaion of key features and reduce feature redundancy.Secondly,the U-I trajectory map of the residential load is extracted and weighted pixelated to obtain the WVI(Weighted pixelated VI)feature matrix through computation,which is applied as the feature coefficient to train the SENet-CNN model.Finally,by virtue of the genetic algorithm,the SENet-CNN model is trained and the hyperparameters of the CNN-SENet model are optimized to improve the model load recognition performance and computational efficiency.The experimental results show that the proposed method can reduce the computational overhead of non-intrusive load identification,accurately identify the residential load categories,and significantly improve the efficiency of non-intrusive load identification.关键词
居民负荷识别/卷积神经网络/NILM/SENet注意力机制/V-I轨迹图Key words
residents load identification/convolutional neural network/NILM/SENet attention mechanism/U-I trajectory diagram引用本文复制引用
沈鑫,王钢,赵毅涛,骆钊,李钊,杨晓华..融合SENet注意力机制和GA-CNN的非侵入式负荷识别方法[J].中国电力,2025,58(5):33-42,10.基金项目
国家重点研发计划(2022YFB2703500) （2022YFB2703500）
国家自然科学基金资助项目(52277104) （52277104）
云南省重点研发计划资助项目(202303AC100003) （202303AC100003）
云南电网有限责任公司科技项目(YNKJXM20220173). This work is supported by the National Key R&D Program of China(No.2022YFB2703500) （YNKJXM20220173）
National Natural Science Foundation of China(No.52277104) （No.52277104）
National Key R&D Program of Yunnan Province(No.202303AC100003)and the Science and Technology Project of Yunnan Power Grid Co.,Ltd.(No.YNKJXM20220173). （No.202303AC100003）
