Abstract:   Non-intrusive load identification can provide information of household loads and improve users' habits, and it is the key technique of smart power utilization. Current non-intrusive load identification methods mainly use steady-state characteristics of loads；usually result in inaccuracy when the loads have similar steady-state characteristics. As various household loads have different peculiarity on switching process, this paper proposes a new identification method based on selected Bayes classifier. Firstly, simulated annealing algorithm is adopted to select the most recognizable characteristics of loads from database for characteristics. Secondly, the flexible Bayes classifier is built on the basis of the selected characteristics and Gaussian kernel density estimation methods. Finally, posterior probability is calculated to identify the load. The measured data shows that the proposed method has high identification accuracy and calculation speed.

Key words:  non-intrusive load identification, flexible Bayes classifier, simulated annealing algorithm, select features