Abstract
Understanding the performance of the heating, ventilation, and air conditioning (HVAC) system in large buildings is a prerequisite for optimizing their energy efficiency. Fine grain performance analysis has not, to our knowledge, received adequate attention thus far. To address this issue we evaluate the thermal comfort and the energy efficiency of a relatively modern HVAC system in a large building based on building-wide high-fidelity environmental data collected via a wireless sensor network over 12 months. Access to fine grain information reveals temporal and spatial dynamics that help quantify the level of (non-)compliance with the system’s control objective and the building’s thermal comfort standards: we find over-conditioning at multiple time scales which offers opportunities for reduced operating cost, and identify building anomalies and ill-conditioned rooms that need maintenance. The paper moreover describes ThermoNet, our hybrid wireless sensor network solution for monitoring a legacy building, which uses duty cycling and adaptive power control to achieve high data yield with low power consumption.
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