A flexible control strategy for energy and comfort aware HVAC in large buildings

Abstract

A data driven framework for the energy and comfort management in large buildings with multiple zones and dynamic occupancy patterns is presented in this paper. For such cases, precise heat conduction models derived using the classical thermal physics laws will be cumbersome. The approach uses the historical data to develop a multi-variable model through Structural Equation Modeling (SEM) so as to identify the relative dominance of the direct and indirect effects of thermal coupling among the neighboring zones, occupancy and the external climate variations on the thermal behavior of the building zones. Based on the information gathered from the SEM, we can predict the return temperatures more accurately, which in turn is employed to incorporate a flexible control strategy for the HVAC system. A controller fed with the temperature error and occupancy error, between the predicted and measured values, regulates the supply air fan speed via VFD motor and outside air damper valve openings. This has resulted in the energy savings while maintaining the occupant thermal comfort at the reasonable levels. The framework proposed has been evaluated using real data collected from an HVAC system of a big airport terminal building. The results show that the accuracy of prediction is relatively higher than with other regression techniques; and that the HVAC system is energy efficient and can ensure occupant comfort on real-time basis in large buildings.