Volume 16: Low Carbon Cities and Urban Energy Systems: Part V

Real-time Building Heat Gains Prediction and HVAC Setpoint Optimization: an Integrated Framework Zu Wang, John Kaiser Calautit, Shuangyu Wei, Paige Wenbin Tien, Liang Xia



This paper proposes an integrated framework to achieve a simultaneous real-time reduction of occupants’ thermal dissatisfaction and room HVAC energy consumption. The framework can optimize the HVAC setpoint temperature according to the internal heat gains predicted by a vision-based approach. When there are no occupants found by cameras, this framework will just turn off HVAC systems to reduce energy consumption. When occupants are present, the framework will determine an optimal setpoint temperature to balance occupants’ thermal satisfaction and room HVAC energy consumption. During the simulated four days in the winter in a temperature climate, the utilization of this framework can lead to a reduction of heating energy by up to 36.8% and occupants’ thermal dissatisfaction by up to 5.26%. During another simulated four days in the summer, the cooling energy savings would range from 3.5% to 33.9%, whilst occupants’ thermal dissatisfaction could be decreased by 0.17-2.89%.

Keywords Artificial intelligence, HVAC setpoint adjustment, Building internal gains prediction, Building energy reduction, Occupants’ comfort satisfaction

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