Household Load Identification Based on Multi-label and Convolutional Neural Networks

Abstract

In low-voltage residential electricity scenarios, simple identification algorithms are difficult to be effective because of the many types of appliances and similar power characteristics. We propose a household load identification method based on multi-label and convolutional neural networks (ML-CNN) to address these problems. Firstly, we analyze the V-I trajectory characteristics of different loads and use the binary images of V-I trajectory mapping as the study features. Secondly, we collect the original steady-state voltage and current data of the combined operation of common household appliances and build a dataset. Finally, we pre-process and multi-label the dataset and input it into the ML-CNN network structure for training and validation. The experimental results show that the average identification accuracy of the ML-CNN method is 97.63%, which is better than the load identification methods such as multi-label k-nearest neighbor (ML-KNN) and support vector machine (SVM).