MARTINI is a sMARt meTer drIveN estImation of occupant-derived Heating, Ventilation and Air Conditioning(HVAC) schedules and energy savings that leverages the ubiquity of energy smart meters and Wi-Fi infrastructure in commercial buildings.

Increasing urbanization puts ever-increasing pressure on cities to prioritize sustainable growth and avoid carbon lock-in, yet available modeling frameworks and tools fall acutely short of robustly guiding such pivotal decision-making at the local level.

In partnership with Bentley Systems, we are working on a digital twin of the UT campus. We are planning to us it to address the uncertainty in energy demand projections, develop future planning scenarios, and provide guidelines for stakeholders and decision makers to prepare the campus for the effects of climate change.

An ideal controller should adapt itself to the preferences of the occupant and the environmental conditions. We developed LightLearn and HVACLearn, which are occupant centric controller (OCC) for lighting and HVAC, respectively, based on Reinforcement Learning (RL).