基于金字塔网络的非侵入式负荷辨识及其隐私保护方案OA北大核心CSTPCD

Smart grids integrate information systems to provide more effective energy-supply solutions.Smart electricity meters are a key part of smart grids,and in-depth research on smart electricity meter data can provide effective support for smart grid management and decision-making.Non-Intrusive Load Monitoring(NILM)technology provides technical support on the demand-side management;however,existing methods require data interaction between users and NILM servers,resulting in the disclosure of private information during this process.To solve these problems,an NILM based on a pyramid network with a two-dimensional Convolutional Neural Network(2D-CNN)is designed,and privacy is protected by homomorphic encryption and secure multiparty computation technology.Privacy-preserving protocols are designed for operators of pyramid networks,such as convolution,full connection,batch normalization,average pooling,ReLU,and upsampling,and are combined to construct a privacy-preserving 2D-CNN pyramid network.The entire process does not restore the original information contained in the data or the intermediate results,thereby protecting the privacy of both parties.The experimental results on the UK-DALE dataset show that the pyramid network based on 2D-CNN can perform well,with an accuracy of 95.81％,and that the privacy-preserving 2D-CNN pyramid network can maintain the inference performance of the 2D-CNN pyramid network while protecting the privacy of the client data and server model parameters with consistent accuracy and recall.At the same time,the privacy-preserving 2D-CNN pyramid network requires a computation time of less than 5 s in a Wide Area Network(WAN)and less than 0.5 s in a Local Area Network(LAN),with a communication volume of only 4.79 MB,making it suitable for real-world scenarios with NILM tasks.