Optimized intelligent control system for indoor thermal comfort and energy management of buildings

Intelligent buildings are important for saving energy and human comfort, which can mainly be achieved with intelligent automated control. Optimized automated control for these buildings is still a challenge to achieve the control target effectively, without disturbing the balance of energy saving and comfort level. Therefore, fuzzy inference model for power consumption and temperature with an intelligent optimizer has been developed in the present work. Power required for actuator system and thermal comfort index are the two control objectives covered for optimized system design. Hybrid Multi-objective Genetic Algorithm (HMOGA) has been used to obtain Pareto optimality solutions for multi-criteria problem. The obtained balanced non-dominated solution with heuristic optimization provides enhanced comfort level with optimum power consumption. This is an important factor for up-to-date decision-making (DM) for management of energy and comfort in buildings. © 2014 IEEE.