Despite widespread adoption and outstanding performance, machine learning models are
considered as “black boxes”, since it is very difficult to understand how such models operate …

In recent years, artificial intelligence (AI) technology has been used in most if not all domains
and has greatly benefited our lives. While AI can accurately extract critical features and …

Background: At the onset of a pandemic, such as COVID-19, data with proper
labeling/attributes corresponding to the new disease might be unavailable or sparse …

Energy consumption in buildings, both residential and commercial, accounts for
approximately 40% of all energy usage in the United States, and similar numbers are being …

Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC
control in energy-efficient buildings. Individual comfort models, compared to average …

Building energy demand response is projected to be important in decarbonizing energy use.
A demand response program that communicates “artificial” hourly price signals to workers …

Our team is proposing to run a full-scale energy demand response experiment in an office
building. Although this is an exciting endeavor which will provide value to the community …

Deep reinforcement learning (DRL) approaches have been used in various application
areas to improve efficiency, optimization, or automation. However, very little is known about …

Ubiquitous deployment of IoT sensors marks a defining characteristic of smart buildings, for
they constitute the source of data on building operation, diagnosis, and maintenance. For …

Optimizing prices for energy demand response requires a flexible controller with ability to
navigate complex environments. We propose a reinforcement learning controller with …