The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a...The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed.展开更多
文摘The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed.
