BUILDING COOLING: A DATA-DRIVEN APPROACH TO ASSESSING THE IMPACT OF WATER-COOLED HVAC SYSTEM DEMAND RESPONSE

ABSTRACT Demand Response (DR) programs play a crucial role in sustainable building practices by promoting efficient energy usage in buildings. These programs offer not only economic benefits to consumers but also enhance grid reliability and contribute to environmental protection. Among various electrical loads, Heating, Ventilation, and Air Conditioning (HVAC) systems are favorable candidates for DR programs due to their significant virtual energy storage capabilities. Leveraging HVAC systems in DR programs enhances DR effectiveness, especially in tropical climates during the summer months. This paper introduces a data-driven approach to assess the impact of HVAC system DR in a multi-zone educational building. The approach leverages the integration of IoT sensors and Machine Learning models implemented in Python to offer a comprehensive analysis of both electrical and thermal aspects of HVAC operations. Validation of predicted DR impact against real-time measurements demonstrates alignment with actual system performance. The study showcases the possibility and the effectiveness of the proposed data-driven approach in assessing the impact of DR activities on energy consumption and thermal variations within the building.