Chou, P.-A.P.-A.ChouChang, R.-I.R.-I.ChangRAY-I CHANG2018-09-102018-09-102013http://www.scopus.com/inward/record.url?eid=2-s2.0-84893562162&partnerID=MN8TOARShttp://scholars.lib.ntu.edu.tw/handle/123456789/380593Efficient use of energy is an important research topic of the smart grid. Load monitoring is an integral part of energy management, convenient information, communication technology, and sensor applications. So far, many monitoring techniques have been developed, and non-intrusive load monitoring is one of them. In order to achieve the complete nonintrusive concept and to adapt to the changes in the environment, this paper proposes the adaptive non-intrusive load monitoring system framework that applied in the monitoring system, taking low frequency acquisition and steady-state feature extraction for reducing its setup costs. The method adopts unsupervised learning, which builds classifier in load state by Gaussian mixture model (GMM)/ Sequential Expectation-maximization (SEM) and does adaptive fine-tuning for the system by online data. The results show that the framework can adapt the changes in the environment and detect new unknown state for providing a more complete on-line monitoring system solution. © 2013 IEEE.Gaussian mixture models; Nilm; Nonintrusive appliance load monitoring; Smart grid; Unsupervised adaptive clustering[SDGs]SDG7Adaptive clustering; Gaussian Mixture Model; Nilm; Non-intrusive appliance load monitoring; Smart grid; Communication channels (information theory); Cybernetics; Feature extraction; Monitoring; Object recognition; Smart power grids; Electric load managementconference paper10.1109/SMC.2013.542