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 Read more
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 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 Read more
Reinforcement learning for urban energy systems & demand response
Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. Reinforcement learning has been utilized to control diverse energy systems Read more
Multi-Agent Reinforcement Learning for demand response & building coordination
We have introduced a new simulation environment that is the result of merging CitySim, a building energy simulator, and TensorFlow, a powerful machine learning library for deep learning. This new simulation environment has the potential for developing building energy scenarios in which machine Read more
