Evolving non-intrusive load monitoring
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
EvoNILM: evolutionary appliance detection for miscellaneous household appliances
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper proposes non-intrusive load-monitoring (NILM) techniques using artificial neural networks (ANN) in combination with genetic algorithm (GA) to identify load demands and improve recognition accuracy of non-intrusive load-monitoring results. The feature extraction method of genetic algorithm can improve the efficiency of load identification and computational time under multiple operations. After comparing various training algorithms and classifiers in terms of artificial neural networks due to various factors that determine whether a network is being used for pattern recognition, the back propagation artificial neural network (BP-ANN) classifier is adopted in the load identification process. Additionally, in combination with electromagnetic transients program (EMTP) simulations and measurements on site, extracting the features of power signatures can lead to accurate load identifications and is a significant feature in smart meters.