Improving Energy and Comfort Management in Smart Buildings

Managing energy consumption in smart buildings has become an increasingly important challenge. Efficient energy management can have a positive impact on both micro and macroeconomics. Moreover, it is essential to ensure that the comfort of smart building residents is maintained at an acceptable level. Optimization algorithms can be used to achieve user convenience while minimizing energy consumption. In this study, we propose an optimization approach that utilizes an agent-based architecture. This architecture comprises intelligent agents that communicate with each other via message exchange in a network structure consisting of three layers: (1) The switch layer monitors user preferences and comfort levels. (2) The coordination layer includes a coordinating agent that determines the optimal timing for electrical appliances to minimize electricity consumption costs and maximize user comfort. (3) The execution layer contains performer agents. Our focus in this research is on the coordination layer with the aim of reducing energy consumption costs and peak average rates, while increasing user comfort to the highest possible level. However, this optimization problem is highly complex due to the large number of electrical devices and their capabilities. To address this, we propose a hybrid method based on genetic and bat algorithms. We evaluated its performance based on objective functions and compared it with recent research on SmartHome and CU-Bems datasets. Our results demonstrate an improvement in performance