Control mechanisms for heating, ventilation and air conditioning in buildings follow set parameters to make conditions in a building more comfortable, but what they save on time can reduce efficiency and increase energy costs, according to Gregory Pavlak, assistant professor of architectural engineering. More sophisticated control models, known as model predictive controllers, can optimize multiple variables to save on energy, operating costs and carbon emissions but can require much more time to find solutions. Penn State researchers developed a method that leverages machine learning to create controls that balance building energy cost, comfort and efficiency while computing at a fast pace. They published their findings in Energy in February. "Detailed model predictive controllers may not be able to compute solutions fast enough for real-time operations in some buildings," Pavlak said.