A SVM Optimized by Particle Swarm Optimization Approach to Load Disaggregation in Non-Intrusive Load Monitoring in Smart Homes

The essence of NILM (Non-intrusive load monitoring) is load decomposition whose results can be further used to improve the energy acquisition system, intelligent power consumption system, and hold out two-way interactive service and intelligent power consumption service. In this paper, a PSO-SVM approach is proposed for load disaggregation in non-intrusive load monitoring. A method in view of the power difference is used to do event detection for switching state of electrical equipment which is proved to be very effective. A multi-feature classification (MFC) on account of PSO-SVM is suggested that it can recognize the switching state of electrical equipment. The simulation results showed that the accuracy rate can attain 95.3% for switched-on state and 96.2% for switched-off state which are more precise than GA-BP and GA-SVM.