Design of A Reliable Algorithmic with Deep Learning and Transfer Learning for Load Combination Recognition

Abstract

Non-intrusive load monitoring system (NILMS) have become a hot research topic. However, the majority of existing methods for NILM are unreliability, especially, the generalization of model and the accuracy of identification in the multi-load with the similar load energy consumption values. This paper proposes a reliable algorithm architecture fused deep learning and transfer learning to improve the reliability of load combination identification model. In the method, a load event outlier detection way based on adaptive sliding-window (ASW) is presented to make better use of experimental data; a feature extractor based on deep convolutional neural networks (DNN) is used to complete the high-quality (Hi-Q) representation of on-line multi-load features; the long short-term memory recurrent neural network (LSTM-RNN) with transfer learning is used to address the generalization of model and the inaccuracy of load combination identification problems. The three data sets from the different domain are employed for training, testing and verifying of the proposed model. The experimental results show that the proposed method can significantly improve the performance of model in NILMS.