An Altered Adaptive Genetic Algorithm for Solving Energy Consumption Problems

2018 ◽  
Author(s):  
Li Liang ◽  
Yong Hou ◽  
Xiaoting Liu
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Adaptive Genetic Algorithm

Self-Adaptive Genetic Algorithm for Modeling Energy Consumption in a Passive House

2018 ◽  
Author(s):  
Silvia Cristina Stegaru ◽  
Mihail-Bogdan Carutasiu ◽  
Emil Ioan Slusanschi ◽  
Cristian Dumitru Tranca ◽  
Nicolae Tapus
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Adaptive Genetic Algorithm ◽  
Passive House ◽  
Self Adaptive

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

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

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.

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