An Efficient Artificial Intelligence Hybrid Approach for Energy Management in Intelligent Buildings

Abstract

Many artificial intelligence (AI) techniques have been embedded into various engineering technologies to assist them in achieving different goals. The integration of modern technologies with energy consumption management system and occupant's comfort inside buildings results in the introduction of intelligent building concept. The major aim of this integration is to manage the energy consumption effectively and keeping the occupant satisfied with the internal environment of the building. The last few couple of years have seen many applications of AI techniques for optimizing the energy consumption with maximizing the user comfort in smart buildings but still there is much room for improvement in this area. In this paper, a hybrid of two AI algorithms called firefly algorithm (FA) and genetic algorithm (GA) has been used for user comfort maximization with minimum energy consumption inside smart building. A complete user friendly system with data from various sensors, user, processes, power control system and different actuators is developed in this work for reducing power consumption and increase the user comfort. The inputs of optimization algorithms are illumination, temperature and air quality sensors' data and the user set parameters whereas the outputs of the optimization algorithms are optimized parameters. These optimized parameters are the inputs of different fuzzy controllers which change the status of different actuators according to user satisfaction.

Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms

“…After the optimization is performed by the FA, the parameters enter the second algorithm-namely, the GA, in this case, for further improving the optimization process. In this work, three parameters called temperature, illumination, and air quality are taken into account that come from the corresponding sensors and are given as inputs to the first optimizer, named the FA, and then the second optimizer, named the GA. All the parameters used in this work follow the standard adopted by the authors in [1][2][3][7][8][9]. The authors in all of the above research works have used the air temperature as the input of the temperature parameter.…”

“…Discomfort I = I − I s I s 2 (7) where I represents the environmental illumination, and I s represents the user-set illumination.…”

“…where T is the environmental temperature, and is the user-set temperature. Similarly, the illumination discomfort is given by Equation (7). DiscomfortI = (7) where I represents the environmental illumination, and represents the user-set illumination.…”

“…Similarly, the illumination discomfort is given by Equation (7). DiscomfortI = (7) where I represents the environmental illumination, and represents the user-set illumination. In a similar fashion, the air quality discomfort can be computed by Equation (8).…”

“…Three parameters in our work to maintain the IEQ of the building depending on the users' demands have been considered. The methodology adopted in this work followed the standard proposed by the authors in [1][2][3][7][8][9]. The authors in all of the above research works have used the air temperature as the input of the temperature parameter, because air temperature measurements are adequately easy in implementation.…”