The Intelligent Environments Laboratory is releasing the first version of the UT Energy App. This app is intended to provide a way to collect information on climate/temperature and consequent comfort levels for students in UT classrooms and other campus buildings. This data will be used directly by the UT… read more
Research
Fault detection and diagnostics of air handling units using machine learning and expert rule-sets
To reduce HVAC energy inefficiencies, fault detection and diagnostics (FDD) has become a growing field of interest. In particular, air handling units (AHU), devices that circulate air and regulate room temperature and humidity, are the primary focus of most HVAC FDD systems. A data-driven FDD for AHUs on a university… read more
Reinforcement Learning in the Built Environment
We develop reinforcement learning techniques for energy efficient operation of buildings and systems without the need for mathematical models. Despite the many advantages of RL for application in the built environment, many challenges remain, and are explored in our research. (1) As RL is a relatively new and emerging field,… read more