ICT for green: how computers can help us to conserve energy
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Feature Extraction of Non-intrusive Load-Monitoring System Using Genetic Algorithm in Smart Meters
ICEBE '11 Proceedings of the 2011 IEEE 8th International Conference on e-Business Engineering
Nonintrusive appliance load monitoring: Review and outlook
IEEE Transactions on Consumer Electronics
Evolving non-intrusive load monitoring
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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To improve the energy awareness of consumers, it is necessary to provide them with information about their energy demand, not just on the household level. Non-intrusive load monitoring (NILM) gives the consumer the opportunity to disaggregate their consumed power on the appliance level. The consumer is provided with information about the energy demand of each individual appliances. In this paper we present an evolutionary optimization algorithm, applicable to NILM purposes. It can be used to detect appliances with a probabilistic power demand model. We show that the detection performance of the evolutionary algorithm can be improved if the single population approach of the evolutionary algorithm is replaced by a parallel population approach with individual exchange and by the introduction of application-oriented pre-processing and mutation methods. The proposed algorithm is tested with Matlab simulations and is evaluated according to the fitness reached and detection probability of the algorithm.