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

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 co...

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Vydáno v:Applied energy Ročník 282; s. 116145
Hlavní autoři: Rocha, Helder R.O., Honorato, Icaro H., Fiorotti, Rodrigo, Celeste, Wanderley C., Silvestre, Leonardo J., Silva, Jair A.L.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.01.2021
Témata:
algorithms
artificial intelligence
batteries
cost effectiveness
Demand planning
electricity costs
Elitist Non-dominated Sorting Genetic Algorithm II
energy
K-means
methodology
regression analysis
Support Vector Regression
K-means
Elitist Non-dominated Sorting Genetic Algorithm II
Demand planning
Support Vector Regression
ISSN:0306-2619, 1872-9118
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Shrnutí: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. •Three Artificial Intelligence techniques were used for energy planning in Smart Home.•The methodology uses Support Vector Regression for power forecast for a day ahead.•The user comfort levels were determined by a K-means clustering technique.•NSGA-II was applied to solve energy planning, taking into account a battery bank.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2020.116145