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Hybrid soft computing algorithmic framework for smart home energy management

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Abstract

Energy management in Smart Home environments is undoubtedly one of the pressing issues in the Smart Grid research field. The aim typically consists in developing a suitable engineering solution able to maximally exploit the availability of renewable resources. Due to the presence of diverse cooperating devices, a complex model, involving the characterization of nonlinear phenomena, is indeed required on purpose. In this paper an Hybrid Soft Computing algorithmic framework, where genetic, neural networks and deterministic optimization algorithms jointly operate, is proposed to perform an efficient scheduling of the electrical tasks and of the activity of energy resources, by adequately handling the inherent nonlinear aspects of the energy management model. In particular, in order to address the end-user comfort constraints, the home thermal characterization is needed: this is accomplished by a nonlinear model relating the energy demand with the required temperature profile. A genetic algorithm, based on such model, is then used to optimally allocate the energy request to match the user thermal constraints, and therefore to allow the mixed-integer deterministic optimization algorithm to determine the remaining energy management actions. From this perspective, the ability to schedule the tasks and allocate the overall energy resources over a finite time horizon is assessed by means of diverse computer simulations in realistic conditions, allowing the authors to positively conclude about the effectiveness of the proposed approach. The degree of realism of the simulated scenario is confirmed by the usage of solar energy production forecasted data, obtained by means of a neural-network based algorithm which completes the framework.

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Notes

  1. GNU stands for GNU Is Not Unix.

  2. http://www.gnu.org/software/glpk/.

  3. http://www.mathworks.it/it/help/gads/index.html.

  4. “Il Meteo” website-http://www.ilmeteo.it.

  5. PVGIS website—http://www.re.jrc.ec.europa.eu/pvgis.

  6. http://www.mathworks.com.

  7. http://www.i2c2.aut.ac.nz/Wiki/OPTI/index.php/Main/HomePage.

  8. Aeeg homesite: http://www.autorita.energia.it/it/inglese/index.htm.

  9. ComeEd home site: http://il.thewattspot.com.

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Authors and Affiliations

  1. 3MediaLabs, Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 , Ancona, Italy

    Marco Severini, Stefano Squartini & Francesco Piazza

Authors
  1. Marco Severini
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  2. Stefano Squartini
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  3. Francesco Piazza
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Corresponding author

Correspondence to Stefano Squartini.

Additional information

Communicated by C. Alippi, D. Zaho and D. Liu.

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Severini, M., Squartini, S. & Piazza, F. Hybrid soft computing algorithmic framework for smart home energy management. Soft Comput 17, 1983–2005 (2013). https://doi.org/10.1007/s00500-013-1118-3

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