Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences
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 algorith...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/2/344 |
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| author | Alejandro Humberto García Ruiz Salvador Ibarra Martínez José Antonio Castán Rocha Jesús David Terán Villanueva Julio Laria Menchaca Mayra Guadalupe Treviño Berrones Mirna Patricia Ponce Flores Aurelio Alejandro Santiago Pineda |
| author_facet | Alejandro Humberto García Ruiz Salvador Ibarra Martínez José Antonio Castán Rocha Jesús David Terán Villanueva Julio Laria Menchaca Mayra Guadalupe Treviño Berrones Mirna Patricia Ponce Flores Aurelio Alejandro Santiago Pineda |
| author_sort | Alejandro Humberto García Ruiz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:42:01Z |
| format | Article |
| id | doaj.art-bd7a532be7bf431abf4cbe6e66ec0a16 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T00:42:01Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-bd7a532be7bf431abf4cbe6e66ec0a162023-12-11T17:45:24ZengMDPI AGSymmetry2073-89942021-02-0113234410.3390/sym13020344Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User PreferencesAlejandro Humberto García Ruiz0Salvador Ibarra Martínez1José Antonio Castán Rocha2Jesús David Terán Villanueva3Julio Laria Menchaca4Mayra Guadalupe Treviño Berrones5Mirna Patricia Ponce Flores6Aurelio Alejandro Santiago Pineda7Facultad de Ingeniería, Universidad Autónoma de Tamaulipas, Tampico 89339, MexicoFacultad de Ingeniería, Universidad Autónoma de Tamaulipas, Tampico 89339, MexicoFacultad de Ingeniería, Universidad Autónoma de Tamaulipas, Tampico 89339, MexicoFacultad de Ingeniería, Universidad Autónoma de Tamaulipas, Tampico 89339, MexicoFacultad de Ingeniería, Universidad Autónoma de Tamaulipas, Tampico 89339, MexicoFacultad de Ingeniería, Universidad Autónoma de Tamaulipas, Tampico 89339, MexicoGraduate Program Division, Tecnológico Nacional de México, Madero 89440, MexicoInformation Technology Engineering, Polytechnic University of Altamira, Altamira 89602, MexicoElectricity 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.https://www.mdpi.com/2073-8994/13/2/344energy optimizationgenetic algorithmsmulti-objective optimizationartificial neural network simulator |
| spellingShingle | Alejandro Humberto García Ruiz Salvador Ibarra Martínez José Antonio Castán Rocha Jesús David Terán Villanueva Julio Laria Menchaca Mayra Guadalupe Treviño Berrones Mirna Patricia Ponce Flores Aurelio Alejandro Santiago Pineda Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences Symmetry energy optimization genetic algorithms multi-objective optimization artificial neural network simulator |
| title | Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences |
| title_full | Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences |
| title_fullStr | Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences |
| title_full_unstemmed | Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences |
| title_short | Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences |
| title_sort | assessing a multi objective genetic algorithm with a simulated environment for energy saving of air conditioning systems with user preferences |
| topic | energy optimization genetic algorithms multi-objective optimization artificial neural network simulator |
| url | https://www.mdpi.com/2073-8994/13/2/344 |
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