A deep learning based non-intrusive household load identification for smart grid in China

Abstract

Load identification have shown significant performance gains in Chinese smart grids. Most existing load identification algorithms are based on electrical characteristics of a steady or transient state, which are therefore limited by feature selection and analysing pattern. To address the above issues, this paper proposes the use of the deep neural network for load identification in a Non-Intrusive Load Monitoring (NILM) test-bed, which is set up by introducing diversified household appliances with different load characteristics, to collect the real-time power usage of appliances in a typical Chinese home. The collected load dataset are then sampled, preprocessed and input to the CNN–LSTM framework for training and features extraction. Next, according to several experiments, the structure of our CNN–LSTM network is determined with reasonable hyper-parameters initialised. Numerical results show that our model is superior to the k-NN, SVM, LSTM and CNN load identification methods, with the average recognition accuracy of 99%, across different kinds of appliances enabled in the typical power grid in China.