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 KeywordsBuilding energy model, Multi-objective optimization, Genetic algorithm, Particle swarm optimization, Pareto-front, Occupancy, comfort.

中文翻译：

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使用多目标优化例程的基于仿真的建筑物能量控制和舒适度管理

具有高度复杂性的建筑能源管理系统必须控制和管理大量执行器和其他设备，并定期评估每个子系统的性能。在本研究中，已经开发出一种控制算法作为一种工程解决方案，用于建筑物中的智能能量控制和舒适度管理。开发了一种基于混合遗传算法的粒子群优化多目标优化程序，以计算供暖，通风，空调和照明系统的最佳设定点水平，以平衡能耗和乘员的舒适度。通过CO2浓度，热和视觉舒适度来评估室内空气质量对乘员的舒适度。已研究出具有不同最佳参数集的案例研究，以计算消耗的能量以及所达到的舒适度。总体居住者的舒适度提高了17％，每天，每周和每月的建筑能耗分别降低了2.5％，7.7％和17.9％。可以将开发的智能控制策略与楼宇自动化系统集成，以实现微调的实时，优化的舒适度管理。关键词：建筑能耗模型，多目标优化，遗传算法，粒子群优化，Pareto-front，入住率，舒适度。