Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences
| Autor: | Jesus David Teran Villanueva, Alejandro Humberto Garcia Ruiz, Aurelio Alejandro Santiago Pineda, Mirna Patricia Ponce Flores, José Antonio Castán Rocha, Julio Laria Menchaca, Mayra Guadalupe Treviño Berrones, Salvador Ibarra Martínez |
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| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Physics and Astronomy (miscellaneous) Computer science 020209 energy General Mathematics Population 02 engineering and technology Multi-objective optimization genetic algorithms energy optimization multi-objective optimization artificial neural network simulator Genetic algorithm 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) education education.field_of_study business.industry lcsh:Mathematics 020208 electrical & electronic engineering Sorting Pareto principle Energy consumption lcsh:QA1-939 Backpropagation Chemistry (miscellaneous) Air conditioning business |
| Zdroj: | Symmetry, Vol 13, Iss 344, p 344 (2021) Symmetry; Volume 13; Issue 2; Pages: 344 |
| ISSN: | 2073-8994 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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