Do occupancy-responsive learning thermostats save energy? A field study in university residence halls

Abstract

Occupant presence and behavior can and should influence energy use in buildings. If occupancy is measured, predicted, or otherwise inferred, building controls can automatically adjust system operating parameters to use less energy without sacrificing user services. However, previous field evaluations and simulation studies appear to have overestimated the energy savings associated with this type of smart control. In this article we present results from a carefully controlled field evaluation of occupancyresponsive learning thermostats installed in every bedroom of three high-rise university residence halls. While a standard practice energy model developed prior to the retrofit estimated 10-25% savings for cooling and 20-50% savings for heating, measurements reveal that the control scheme only reduced energy consumption by 0-9% for cooling, and by 5-8% for heating for normal operation during academic periods. However, for non-academic periods when the residence halls were sparsely populated, the scheme reduced cooling energy consumption by 20-30%. We analyzed these observations in relation to occupancy patterns, room temperature records, ambient conditions, and equipment run time. The findings provide novel insight about how to improve field evaluations and refine model assumptions to better predict the impact of occupancy-responsive thermostat controls. Notably, while analysts often use fractional building occupancy trends to simulate building energy performance, this study highlights the importance of accounting accurately for both the temporal and spatial variation of vacancy events throughout a building.

Characterizing Variations in the Indoor Temperature and Humidity of Guest Rooms with an Occupancy-Based Climate Control Technology

“…The occupancy sensor used in this study incorporated the Passive Infrared (PIR) technology to detect the presence of the room's occupancy in a binary manner (i.e., occupied, unoccupied), which has been used in previous literature to measure occupancy [23,24]. Even though the rooms are equipped with the card-key systems, the hotel's Property Management System (PMS) does not record guest occupancy status as detected in their card-key systems.…”

On Occupant Behavior and Innovation Studies Towards High Performance Buildings: A Transdisciplinary Approach

“…Having a long historical background and large number of installations all around the world, current state-of-the-art thermostats are subject to discussions involving both the technology and human dimensions of the 'art of heat'. The literature shows variety of ranges for potential energy savings with the effective use of them, which can vary in the range of 10-25% [113][114][115]. Besides, thermostats are also studied within the context of thermal comfort, indoor air quality, and behavioral economics [116,117].…”

Remote control‐based energy management for energy savings in a central heating system

“…Pritoni et al . presented the results from a carefully controlled field evaluation of occupancy‐responsive learning thermostats installed in every bedroom of three high‐rise university residence halls.…”

“…The integration of meters and sensors with existing BEMS is critically appraised, especially with regard to communication technologies and protocols such as ModBus, M-Bus, Ethernet, Cellular, ZigBee, WiFi, and BACnet. Pritoni et al [35] presented the results from a carefully controlled field evaluation of occupancy-responsive learning thermostats installed in every bedroom of three high-rise university residence halls.…”