Analysis of multi‐objective optimization: a technical proposal for energy and comfort management in buildings

Summary Buildings around the world account for about one‐third of the energy consumption. Enough energy is required to maintain the comfort level for the occupants. Recently, the rise in global temperature resulting in climate change is associated with the comfort level for both outdoor and indoor of the buildings. Thus, providing acceptable comfort levels within buildings has become significant. The comfortable indoor environment of building requires energy for the operation of various appliances. A smart and energy efficient approach is the need of an hour to reduce energy consumption and attain comfortable indoor environment. The building energy and comfort management system (BECMS) model incorporating trade‐off between energy consumption and comfort has already been focused in previous studies. However, limited analyses have been observed in comparing the most efficient population‐based algorithm for BECMS. In this paper, a comparative study has been carried out using three different optimization techniques including multi‐objective genetic algorithm (MOGA), hybrid MOGA (HMOGA), and multi‐objective particle swarm optimization method (MOPSO) for optimal energy and comfort management in buildings. These optimization techniques have been widely employed to solve various optimization problems. The significant contribution of this article identifies the best suited algorithm in attaining best optimized solution of energy and comfort in a building. The comparative analysis of the three optimization techniques shows that MOPSO outperforms and attains maximum comfort level and higher energy savings. The detailed block architecture of the proposed building energy and comfort management system framework.