Abstract
Smart homes of the future will include automation systems that will provide lower energy consumption costs and comfortable environments to end users. In this work we propose an algorithm, based on the “Mixed-Integer Linear Programming” paradigm, able to find the optimal task and energy scheduling in realistic residential scenarios, in order to reduce costs and satisfy the user requirements at the same time. Both the static and the dynamic case studies have been addressed on purpose and results obtained from computer simulations seem to confirm the effectiveness of the idea.
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De Angelis, F. et al. (2012). Optimal Task and Energy Scheduling in Dynamic Residential Scenarios. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_73
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DOI: https://doi.org/10.1007/978-3-642-31346-2_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31345-5
Online ISBN: 978-3-642-31346-2
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