A Non-intrusive home load identification method based on adaptive reinforcement learning algorithm

Abstract

At present, the load data collection of residential users mainly starts from the lower acquisition frequency, so the non-intrusive home load identification method based on low frequency sampling has attracted wide attention. However, low-frequency sampling has a low recognition accuracy when the training data set is small. Therefore, a non-intrusive home load identification method based on adaptive KNN reinforcement learning algorithm is proposed. The method firstly analyzes the state of the electrical appliance by KNN to obtain the initial HMM model, and then solves the HMM model by adaptive KNN reinforcement learning algorithm to obtain the optimal state transition strategy. This method reduces the model pair data. The dependence improves the recognition accuracy of the model and the adaptability to new data. Finally, the experimental verification is carried out by the low frequency data set AMPds. The results show that the method improves the state recognition accuracy of the electrical appliance and enhances the adaptability of the algorithm to new data.