Abstract
The household appliances are becoming each time more connected to the others and to the internet. Beyond the necessity of improving and increasing the clean and renewable power sources, there is the urge for using the available power sources in more efficient manners. When the concepts of Internet of Things and Computational Intelligence converge, it is enabled a whole field of technologies aiming Smart Home functionality as dweller’s comfort, tasks automation and energy saving. This work proposes a Smart Home appliances usage recommendation concept, based on multi-objective optimization to generate balanced recommendations regarding energy usage reduction and dweller’s comfort. Those recommendations are also segmented according to the comfort priority of the dwellers in the room. In order to evaluate the priorities, it was implemented a Markov Chain to generate new dweller’s presence data from the data set. It was also studied the potential of power saving of the Binary Multi Objective Particle Swarm Optimization over contexts extracted from the data. Results showed that the recommendations have an average power saving potential of 9,76% as well as the possibility of deliver recommendations with different comfort levels according to the priority of the dwellers present in the room.
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Acknowledgment
The authors are grateful to the Brazilian institutions: FACEPE - Fundacao de Amparo a Ciencia e Tecnologia do Estado de Pernambuco, CIn - Centro de Informatica da UFPE, LIVE - Laboratorio de Inovacao Veicular da UFPE/FCA and UFPE - Universidade Federal de Pernambuco, for the support of this research.
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Feitosa, A., Lacerda, H., Silva-Filho, A. (2019). Smart Home Appliances Usage Recommendation Using Multi-objective Optimization. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_40
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DOI: https://doi.org/10.1007/978-3-030-30241-2_40
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