Data-driven approach for assessing ventilation thermal performance based on main heat meter readings

The significance of energy management and optimization in modern building operations cannot be overstated. However, many buildings lack the necessary connectivity, leading to underutilized metered data and possible inefficiencies. This study develops a data-driven method to assess ventilation system thermal performance using main heat meter readings, clustering techniques, and identifying distinct heat use profiles on a scale of a day. The main hypothesis of this research is that in inefficient systems, especially during warmer periods, the total heat use change mirrors the heat consumed by ventilation systems. Conducted on 6 university buildings, our findings revealed that during transitional periods, such as mornings and evenings, ventilation systems significantly influence heat use. In buildings with less efficient heat exchangers or suboptimal setpoints, this impact was clearer. These results provide actionable insights for building operators and contribute to the conversation on data-driven energy efficiency in building management.