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

• Published in: Symmetry (Basel) Vol. 13; no. 2; p. 344
• Main Authors: García Ruiz, Alejandro Humberto, Ibarra Martínez, Salvador, Castán Rocha, José Antonio, Terán Villanueva, Jesús David, Laria Menchaca, Julio, Treviño Berrones, Mayra Guadalupe, Ponce Flores, Mirna Patricia, Santiago Pineda, Aurelio Alejandro
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2021
• Subjects: Air conditioning; Artificial intelligence; artificial neural network simulator; Back propagation; Back propagation networks; Computer simulation; Economic impact; Energy consumption; Energy management; energy optimization; Experimentation; Genetic algorithms; Linear programming; multi-objective optimization; Multiple objective analysis; Neural networks; Objectives; Optimization; Optimization algorithms; Population; Sorting algorithms; Temperature; User satisfaction
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym13020344

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.