基于时间卷积神经网络的非侵入式居民用电负荷分解方法

非侵入式负荷分解技术通过从主表信息中恢复出用电侧单个用电设备的状态,可以准确地刻画用户用电画像,为用户侧精细化管理发挥重要作用.针对目前人工神经网络模型在负荷分解中存在的分解精度不高、训练效率低下等问题,文章构建了基于时间卷积神经网络(temporal convolutional neural network,TCN)...

Non-Intrusive Residential Electricity Load Disaggregation Based on Temporal Convolutional Neural Network

Non-intrusive load disaggregation can accurately portray the user’s power consumption portrait by recovering the information of single electrical equipment on the power consumption side from the total electric meter information, which plays an important role in the refined management of the consumers. Aiming at the problems of low decomposition accuracy and low training efficiency of current artificial neural network models in load decomposition, this paper studies and builds a non-intrusive load disaggregation model based on temporal convolutional neural network. By analyzing the power consumption of the device, the dilated causal convolution is applied to perform convolution operations on the power sequence of electric meter and to expand the receptive field and extract richer features. The network training efficiency is improved by adding residual connections, weight normalization layers and optimizing training data window. Finally, the constructed model is tested on the optimized UKdale data set. The experimental results show that mean absolute error, root mean square error, and relative error are all in a relatively small range, and the time complexity analysis further shows that the model has a shorter training time without losing the load decomposition accuracy.