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Open AccessArticle

Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

1
Facultad de Ingeniería, Universidad Autónoma de Tamaulipas, Tampico 89339, Mexico
2
Graduate Program Division, Tecnológico Nacional de México, Madero 89440, Mexico
3
Graduate Program Division, Instituto Tecnológico de Ciudad Madero, Cd., Madero 89440, Mexico
4
Information Technology Engineering, Polytechnic University of Altamira, Altamira 89602, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Jan Awrejcewic
Symmetry 2021, 13(2), 344; https://doi.org/10.3390/sym13020344
Received: 27 January 2021 / Revised: 16 February 2021 / Accepted: 16 February 2021 / Published: 20 February 2021
(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)
Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving. View Full-Text
Keywords: energy optimization; genetic algorithms; multi-objective optimization; artificial neural network simulator energy optimization; genetic algorithms; multi-objective optimization; artificial neural network simulator
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MDPI and ACS Style

García Ruiz, A.H.; Ibarra Martínez, S.; Castán Rocha, J.A.; Terán Villanueva, J.D.; Laria Menchaca, J.; Treviño Berrones, M.G.; Ponce Flores, M.P.; Santiago Pineda, A.A. Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences. Symmetry 2021, 13, 344. https://doi.org/10.3390/sym13020344

AMA Style

García Ruiz AH, Ibarra Martínez S, Castán Rocha JA, Terán Villanueva JD, Laria Menchaca J, Treviño Berrones MG, Ponce Flores MP, Santiago Pineda AA. Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences. Symmetry. 2021; 13(2):344. https://doi.org/10.3390/sym13020344

Chicago/Turabian Style

García Ruiz, Alejandro H.; Ibarra Martínez, Salvador; Castán Rocha, José A.; Terán Villanueva, Jesús D.; Laria Menchaca, Julio; Treviño Berrones, Mayra G.; Ponce Flores, Mirna P.; Santiago Pineda, Aurelio A. 2021. "Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences" Symmetry 13, no. 2: 344. https://doi.org/10.3390/sym13020344

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