Non-intrusive Load Disaggregation Based on Kernel Density Estimation

2017 2nd Asia Conference on Power and Electrical Engineering

能源学;电工学

Wang, Sen^1 ; Yang, Dongsheng^1 ; Guo, Chuchen^1 ; Du, Shengxian^1

College of Information Science and Engineering, Northeastern University, Shenyang, China^1

关键词: Electricity load;    High frequency HF;    Kernel Density Estimation;    Load decompositions;    Load disaggregation;    Nonintrusive load monitoring;    Reference modeling;    Reference models;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/199/1/012078/pdf
DOI  :  10.1088/1757-899X/199/1/012078

来源: IOP

【 摘 要 】

Aiming at the problem of high cost and difficult implementation of high frequency non-intrusive load decomposition method, this paper proposes a new method based on kernel density estimation(KDE) for low frequency NILM (Non-intrusive load monitoring). The method establishes power reference model of electricity load in different working conditions and appliance's possible combinations first, then probability distribution is calculated as appliances features by kernel density estimation. After that, target power data is divided by step changes, whose distributions will be compared with reference models, and the most similar reference model will be chosen as the decomposed consequence. The proposed approach was tested with data from the GREEND public data set, it showed better performance in terms of energy disaggregation accuracy compared with many traditional NILM approaches. Our results show good performance which can achieve more than 93% accuracy in simulation.