Resident non-invasive load identification algorithm based on prior statistical model

Abstract

In this paper, a non-intrusive load identification algorithm for residents based on prior knowledge and statistical learning model is proposed to solve the problem of insufficient electric heating subdivision capability in traditional identification technology. In this paper, the electric heating subdivision research is carried out for the auxiliary heating equipment of washing machine, electric kettle, electric rice cooker, electric water heater. The subdivision of auxiliary heating equipment is realized through the equipment operation association algorithm, and the model training of non-auxiliary heating equipment classification is realized based on the limited feedback information of users and expert annotation. The experimental results show that the technical framework proposed in this paper realizes the subdivision of electric heating equipment on the basis of the event detection load identification algorithm and F1 socre above 0.9 is achieved in the decomposition of operation state.

Keywords

• non-intrusive load monitoring (nilm)
• event detection
• subdivision of electric heating equipment
• statistical analysis
• gaussian mixture model (gmm)
• support vector machine (svm)