Sustainable building climate control with renewable energy sources using nonlinear model predictive control

Sustainable energy sources are promising solutions for reducing carbon footprint and environmental impacts within the building sectors. Reducing consumption while ensuring thermal comfort occupants is now an essential task in climate control. Two possible ways to cut down implementing renewable sustainable sources, such as solar geothermal energy, adopting model predictive control method. In this study, we develop a novel nonlinear (NMPC) framework of buildings with systems minimize electricity costs. A dynamic systems, including temperature, humidity, comfort, heat pump, panels, first constructed based on mass balance equations. The then integrated into proposed NMPC framework, which iteratively solves programming problem generate optimal inputs footprints sustainability. simulation case study controlling located Cornell University campus conducted demonstrate capability reduce using framework. results show could efficiently total cost constraint violation 12.9% no more than 0.2 predicted mean value index different seasons. Implementing storage component by 19%. indicate better sustainability smart