Optimizing HVAC operation in commercial buildings: A genetic algorithm multi-objective optimization framework

Heating, Ventilation, and Air Conditioning (HVAC) systems account for a large share of the energy consumed in commercial buildings. Simple strategies such as adjusting HVAC set point temperatures can lead to significant energy savings at no additional financial costs. Despite their promising results, it is currently unclear if such operation strategies can have unintended consequences on other building performance metrics, such as occupants' thermal comfort and productivity. In this paper, a genetic algorithm multi-objective optimization framework is proposed to optimize the HVAC temperature set point settings in commercial buildings. Three objectives are considered, namely energy consumption, thermal comfort, and productivity. A reference medium-sized office building located in Baltimore, MD, is used as a case study to illustrate the framework's capabilities. Results highlight important tradeoffs between the considered metrics, which can guide the design of effective and comprehensive HVAC operation strategies.