Non-Intrusive Electric Appliances Load Monitoring System -Experiment for Real Household-

Abstract

This paper presents applying results of four estimation algorithms of non-intrusive monitoring system for real household. We conclude that all algorithms have practicable ability. 1) support vector machine(SVM): SVM was used to estimate ON/OFF states for fluorescent and refrigerator. SVM has the performance equivalent to best performance of sigmoid function networks(SFN). However, SVM has high estimating ability constantly. 2) RBF networks(RBFN): RBFN was used to estimate power consumption for air conditioner. RBFN has the performance equivalent to best performance of SFN. However, RBFN has high estimating ability constantly. 3) step change detection method(SCD): SCD was used to estimate ON/OFF states and power consumption for IH cooking range. SCD does not need the necessary learning process for SFN and has higher estimating ability than SFN. 4) spectrum reference method(SRM): SRM was used to estimate working conditions for rice cocker and washing machine. SRM is able to estimate these working conditions that cannot be estimated by earlier methods. © 2004, The Institute of Electrical Engineers of Japan. All rights reserved.