Building Energy and Comfort Optimization

Buildings account for roughly 40% of global energy use, and making them more efficient without sacrificing the comfort of the people inside them is one of the central engineering challenges of the coming decades. Researchers study how heat moves through building envelopes, how occupants actually behave versus how designers assume they will, and how shifting climate patterns will alter energy demand over a structure's lifetime—questions that span physics, data science, and human factors simultaneously. Techniques like model predictive control and artificial neural networks are increasingly used to anticipate a building's needs in real time, adjusting heating, cooling, and ventilation before conditions drift outside comfortable bounds rather than reacting after the fact. Open questions include how to build models accurate enough to be useful while remaining computationally light enough to run on building management hardware, and how to account for the unpredictable, context-dependent ways people interact with thermostats, windows, and shading devices.

Works

146,640

Total citations

1,665,689

Keywords

Building Energy ConsumptionThermal ComfortEnergy SimulationModel Predictive ControlSustainable BuildingsOccupant Behavior