Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home

Ubiquitous computing provides convenient and fast information distribution service by using sensor nodes and wireless network, and a good household appliance recognition system will allow users to effectively understand the household appliance usage and develop habits of power preservation. At present, smart meters convert the information of traditional electric meters to easily accessible digital information, based on which, the household appliance recognition service can be carried out. However, it is different from video or audio recognition service, when a variety of electrical appliances run, they will all have individual impact on power consumption, thereby resulting in the difficulties in recognition. Presently, the complex current information arising from many household appliances also increases the difficulty in extracting power features. For addressing the challenge, this study proposes a set of multi-appliance recognition system, which designs a single smart meter using a current sensor and a voltage sensor in combination with a microprocessor to meter multi-appliances. After fuzzy processing of the power information are read through the smart meter and extraction of the power features, electric appliances are classified using the hybrid Support Vector Machine/Gaussian Mixture Model (SVM/GMM) classification model. GMM is mainly used describe the wave distribution situation according to the current information, so as to find the power similarity; while SVM is used to classify the power features of different electric appliances, so as to summarize the classification properties of different electric appliances and establish a classification model. Finally, the household appliances that are in use can be recognized with the household power supply terminal, and their information can be reported to users through wired or wireless network to achieve ubiquitous recognition service. This study has developed and implemented this system prototype, and is used to prove its design theory. (c) 2012 Elsevier Inc. All rights reserved.