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
Research
UT Energy App – Privacy Policy
Fault detection and diagnostics of air handling units using machine learning and expert rule-sets
Reinforcement Learning in the Built Environment
Reinforcement learning for urban energy systems & demand response
Multi-Agent Reinforcement Learning for demand response & building coordination
IEA-EBC Annex 79: Occupant Centric Design and Operation of Buildings
Tags
air handling unit
Annex 79
architecture
artificial neural network
Bluetooth
Community engaged research
earthquakes
environmental monitoring
fault detection and diagnostics
HVAC
integrated design
intelligent energy management
Lighting Control
machine learning
Megan McHugh
multi-agent systems
Occupancy
Occupant Centered Control
Reinforcement Learning
Review
Smart Building
smart city
teaching
Thermal Comfort
