基于选择性贝叶斯分类的非侵入式负荷识别方法

非侵入式负荷识别可以提供用电信息，帮助用户改善用电习惯，是智能用电的关键技术。现有非侵入式负荷识别方法主要基于负荷的稳态特征进行识别，对稳态特征近似的负荷识别率不高。针对此问题，该文结合各类家用负荷在投切过程中的不同特点，提出了一种基于选择性贝叶斯分类的识别方法。首先，利用模拟退火算法从特征库中依据负荷特点选择出对于各类负荷最具辨识度的特征；然后，根据选择的特征和高斯核密度估计方法建立灵活贝叶斯分类器；最后，通过计算各负荷的后验概率对负荷进行识别。经实测数据检验，该方法具有良好的识别精度和计算速度。

Non-intrusive Load Identification Method Based on Selected Bayes Classifier

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.This work is supported by National Natural Science Foundation of China （No. 51477105 ）.