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An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes

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Listed:
  • Rocha, Helder R.O.
  • Honorato, Icaro H.
  • Fiorotti, Rodrigo
  • Celeste, Wanderley C.
  • Silvestre, Leonardo J.
  • Silva, Jair A.L.

Abstract

A new methodology, that combine three different Artificial Intelligence techniques, is proposed in this paper to solve the energy demand planning in Smart Homes. Conceived as a multi-objective scheduling problem, the new method is developed to reach the compromise between energy cost and the user comfort. Using an Elitist Non-dominated Sorting Genetic Algorithm II, the concept of demand-side management is applied taking into account electricity price fluctuations over time, priority in the use of equipment, operating cycles and a battery bank. The demand-side management also considers a forecast of a distributed generation for a day ahead, employing the Support Vector Regression technique. Validated by numerical simulations with real data obtained from a smart home, the user comfort levels were determined by the K-means clustering technique. The efficiency of the proposed Artificial Intelligence combination was proved according to a 51.4% cost reduction, when Smart Homes with and without distributed generation and battery bank are compared.

Suggested Citation

  • Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    • Handle: RePEc:eee:appene:v:282:y:2021:i:pa:s0306261920315555
    DOI: 10.1016/j.apenergy.2020.116145
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