This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance even...This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance events using a goodness of fit test and then pair the on-off events.Then the multi-dimensional features are extracted to establish a feature library.In the first stage identification,several groups of events for the appliance have been divided,according to three features,including phase,steady active power and power peak.In the second stage identification,a“one against the rest”support vector machine(SVM)model for each group is established to precisely identify the appliances.The proposed method is verified by using a public available dataset;the results show that the proposed method contains high generalization ability,less computation,and less training samples.展开更多
：This paper points out the limitations of both parametric and geometric model methodscommonly applied to generate operational sketches in CAPP. The ideas of multi-level graphic library areintroduced and a new generat...：This paper points out the limitations of both parametric and geometric model methodscommonly applied to generate operational sketches in CAPP. The ideas of multi-level graphic library areintroduced and a new generative method based on this library which combines parametric method withgeometric model method is presented. The principles and characteristics of the construction of the library aredescribed in detail and the feature data structure corresponding with the library is also presented.展开更多
基金supported by the National Science Foundation of China(U2166209,52007126)the Science and Technology Project of State Grid Tibet Electric Power Company(52311020009X)。
文摘This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance events using a goodness of fit test and then pair the on-off events.Then the multi-dimensional features are extracted to establish a feature library.In the first stage identification,several groups of events for the appliance have been divided,according to three features,including phase,steady active power and power peak.In the second stage identification,a“one against the rest”support vector machine(SVM)model for each group is established to precisely identify the appliances.The proposed method is verified by using a public available dataset;the results show that the proposed method contains high generalization ability,less computation,and less training samples.
文摘：This paper points out the limitations of both parametric and geometric model methodscommonly applied to generate operational sketches in CAPP. The ideas of multi-level graphic library areintroduced and a new generative method based on this library which combines parametric method withgeometric model method is presented. The principles and characteristics of the construction of the library aredescribed in detail and the feature data structure corresponding with the library is also presented.
