Support vector machine based methods for non-intrusive identification of miscellaneous electric loads

Miscellaneous electric loads (MELs) currently consume more electricity than any other single major category of electric appliances. MELs provide valuable energy consumption and performance information which can be utilized to meet the raising needs and opportunities of energy saving, demand response, peak shaving, and building management. A reliable intelligent method to identify different MELs is a prerequisite of all purposes. A support-vector-machine (SVM) based hybrid identification method of MELs is proposed in this paper. Studies on applying only SVM as well as a combination of SVM and supervised Self-Organizing Map (SSOM) are presented. SSOM first cluster a large number of MELs into several classes. MELs with similar feature values fall into the same class. SVM is then utilized to identify similar MELs. The proposed method shows satisfactory accuracy in tests using real-world data.