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Abstract
Buildings are a significant contributor to global warming where almost 20% of total energy consumption worldwide is dedicated to buildings heating, ventilating, and air-conditioning (HVAC) systems. Demand response (DR) could substantially reduce energy consumption and the associated environmental impacts especially during peak-electricity-demand hours. HVAC systems have a great potential to participate in DR programs. However, since HVAC systems operation significantly contributes to building occupants health and well-being, it is essential to find a tradeoff between energy consumption by HVAC systems and occupants thermal comfort in buildings. Unfortunately, DR measures, if are not accompanied by proper thermal comfort and IEQ measures, could cause serious health issues including reduced performance and sick building syndrome. To avoid this, multiple researchers tried to optimize buildings energy consumption/cost with occupants thermal comfort. Conventionally, buildings HVAC systems are controlled through keeping zone temperatures in a desired range. However, this control strategy could result in excessive energy consumption while not necessarily satisfying most of building occupants due to the lack of scaling occupants thermal preferences and their adaptive behavior. Others proposed the use of PMV/PPD indices or ASHRAE Standard 55 graphical comfort zone to ensure occupants thermal comfort while optimizing building energy consumption. However, based on the literature, these static thermal comfort indices do not guarantee occupants thermal comfort. On the other hand, optimizing energy consumption or energy cost only either underestimates the impact of real time price (RTP) for electricity on users consumption behavior, or misrepresents the real amount of energy consumption required by necessary services in buildings.Current research proposes a weighted-sum, genetic algorithm (GA) method to optimize occupants mean thermal preferences with energy consumption on the university of Georgias (UGA) campus, where weights are a function of RTP. Initially, an extensive field study is conducted on UGA campus to model the occupants thermal sensation and preferences in real-world conditions. Building energy consumption for space cooling is simulated using eQuest software. Different DR strategies versus RTP are benchmarked. The results show that this optimization method is a promising tool in maximizing occupants thermal comfort while minimizing building energy consumption during DR events on UGA campus.