Simulation based energy control and comfort management in buildings using multi-objective optimization routine

Abstract

Building energy management systems with high-level of sophistication have to control and manage a large set of actuators and other equipment and evaluate performance of each and every-subsystem on periodic basis. In the present study, a control algorithm has been developed as an engineered solution for intelligent energy control and comfort management in buildings. A hybrid genetic algorithm - particle swarm optimization based multi-objective optimization routine is developed to compute the optimal set-point level of heating, ventilation, and air conditioning and lighting systems with a view to balancing energy consumption and occupants' comfort. Occupants' comfort is evaluated for indoor air quality as CO2 concentration, thermal and visual comfort. Case studies with a different set of optimal parameters have been worked out to calculate the amount of energy consumed as well as comfort level achieved. Overall occupants' comfort was improved by 17% and daily, weekly and monthly building energy consumption was reduced by 2.5%, 7.7%, and 17.9%, respectively. The developed intelligent control strategy can be integrated with building automation systems to achieve finely tuned real-time optimized comfort management.