TSILNet:A novel hybrid model for energy disaggregation based on two-stage improved TCN combined with IECA-LSTM  

作　　者：Ziwei Zhu Mengran Zhou Feng Hu Kun Wang Guangyao Zhou Weile Kong Yijie Hu Enhan Cui 

机构地区：[1]School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan,232001,Anhui,China

出　　处：《Building Simulation》2024年第11期2083-2095,共13页建筑模拟（英文）

基　　金：the National Natural Science Foundation Project(Grant No.52374177);Anhui Provincial Natural Science Foundation Energy Internet Joint Fund Key Project(Grant No.2008085UD06);State Grid Anhui Electric Power Co.,Ltd.Fuyang Power Supply Company Science and Technology Project(Grant No.SGAHFY00TKJS2310510)。

摘　　要：Non-intrusive load monitoring(NILM)technology aims to infer the operation information of electrical appliances from the total household load signals,which is of great significance for energy conservation and planning.However,existing methods are difficult to effectively capture the complex nonlinear features of the power consumption flow,which affects the energy disaggregation accuracy.To this end,this paper designs a method based on temporal convolutional network(TCN),efficient channel attention(ECA),and long short-term memory(LSTM).The method first creatively proposes a two-stage improved TCN(TSTCN),which overcomes its problems of extracting discontinuous information and poor correlation of long-distance information while enhancing the ability to extract high-level load features.Then a novel improved ECA attention mechanism(IECA)is embedded,which is also combined with the skip connection technique to pay channel-weighted attention to important feature maps and promote information fusion.Finally,the LSTM with strong temporal memory capability is introduced to learn the dependencies in the load power sequence and realize load disaggregation.Experiments on two real-world datasets,REDD and UK-DALE,show that the proposed model significantly outperforms other comparative NILM algorithms and achieves satisfactory tracking with the actual appliance operating power.The results show that the mean absolute error(MAE)of all appliances decreases by 18.67%on average,and the F1 score improves by 38.70%.

关 键 词：deep learning non-intrusive load monitoring(NILM) energy disaggregation temporal convolutional network(TCN) attention mechanism long short-term memory(LSTM) 

分 类 号：TU11[建筑科学—建筑理论]