Date Time Deep Learning Trains Buildings to Optimize Efficiency In the United States, residential and commercial buildings account for nearly 40 percent of all energy consumption. About 40 percent of that energy use is from heating, ventilation, and air-conditioning (HVAC) systems. An emerging efficiency method for buildings called model predictive control (MPC) could reduce HVAC energy use by up to 50 percent without impacting occupant comfort. But the method has been hampered by high costs associated primarily with software and computing. Now, a team of scientists at Pacific Northwest National Laboratory (PNNL) has developed a new deep learning approach that uses building data and physics knowledge to train the MPC. The learning approach allows nonexperts to optimize control of a building’s energy systems without the need for additional computing power and proprietary software.