Abstract
The computational intelligence community has invested many efforts in the last few years on the challenging problem of automatic task and energy resources scheduling in smart home contexts. Moving from a recent work of some of the authors, jointly considering the electrical and thermal comfort needs of the user, in this paper a nonlinear optimization framework, namely “Mixed-Integer Nonlinear Programming”, is proposed on purpose. It allows dealing with nonlinearities resulting from the constraints imposed by the involved building thermal model, which was not feasible in the original linear approach. Performed computer simulations related to a realistic domestic scenario have shown that a certain improvement is attainable in terms of satisfaction of user thermal requirements, attaining at the same time an enhanced overall energy cost reduction with respect to the non-optimized scheduling strategy.
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Marco, S., Squartini, S., Surace, G.P., Piazza, F. (2014). Smart Home Task and Energy Resource Scheduling Based on Nonlinear Programming. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_18
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DOI: https://doi.org/10.1007/978-3-319-04129-2_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04128-5
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