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.
This paper is an improvement and follow up paper of the previous work "Evolving Non-Intrusive Load Monitoring".
- D. Egarter, W. Elmenreich. EvoNILM - Evolutionary Appliance Detection for Miscellaneous Household Appliances Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation, Amsterdam, July 2013.