Virtual PMV sensor towards smart thermostats: Comparison of modeling approaches using intrusive data

Smart thermostats are considered an effective digital technology for building energy efficiency. This study proposes a virtual predicted mean vote (PMV) sensor for smart thermostats for indoor thermal comfort and energy-efficient building operation and control. The proposed virtual sensor can observe the representative PMV for the occupied zone, considering the systematic errors caused by the spatial difference between the wall-mounted thermostat and the occupied zone. Virtual PMV sensor-based smart thermostat has the potential to improve energy efficiency and thermal comfort by observing unmeasured thermal comfort within the occupied zone based on variables obtained from an existing wall-installed thermostat. To cover systematic errors for general applications, intrusive measurements using a commissioning period are necessary, even in the short term. Thus, conventional modeling approaches can be used with limited intrusive data to develop an accurate in-situ virtual PMV sensor during operation. In the application into a small-sized office building, the real systematic errors showed up to −2.4 °C among ten different thermostat locations in the target office zone. The in-situ virtual sensor could accurately observe the PMV for an occupied zone for different air-conditioning periods (air-conditioned working hours, non-air-conditioned working hours, and closing hours) with the best accuracy of 0.05 mean absolute error (MAE) using one-day intrusive dataset. Based on comparing white-box, black-box, and gray-box virtual sensors in the application, this study recommends using the gray-box modeling approach to leverage the synergetic effect between data and knowledge for better in-situ virtual sensor modeling under a limited intrusive dataset.