Neural network design
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
A conjoint pattern recognition approach to nonintrusive load monitoring
A conjoint pattern recognition approach to nonintrusive load monitoring
The ANNIGMA-wrapper approach to fast feature selection for neuralnets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Input feature selection for classification problems
IEEE Transactions on Neural Networks
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In response to the governmental policy of saving energy sources and reducing CO2, and carry out the resident quality of local; this paper proposes a new method for a non-intrusive load-monitoring (NILM) system in smart home to implement the load identification of electric equipments and establish the electric demand management. Non-intrusive load-monitoring techniques were often based on power signatures in the past, these techniques are necessary to be improved for the results of reliability and accuracy of recognition. By using neural network (NN) in combination with genetic programming (GP) and turn-on transient energy analysis, this study attempts to identify load demands and improve recognition accuracy of non-intrusive load-monitoring results. The turn-on transient energy signature can improve the efficiency of load identification and computational time under multiple operations.