New Software Uses Machine Learning to Quickly Identify Buildings that Waste Electricity

Commercial buildings account for more than 72% of all energy consumption in the United States, yet the majority of buildings waste significant amounts of energy due to minor system errors, like broken thermostats or improperly tuned HVAC systems. Although these faults result in costly electricity use and the generation of unnecessary CO2 emissions, they often go unaddressed because they are difficult to diagnose and are rarely considered urgent. But research supported by the Siebel Energy Institute has created a software platform that quickly identifies which structures would yield the most energy savings from the least expensive repairs. Led by Kameshwar Poolla, a professor of Mechanical Engineering at the University of California, the research focuses on identifying buildings that would use markedly less energy with low-cost efficiency improvements, such as repair of HVAC infrastructure or recalibration…


Link to Full Article: New Software Uses Machine Learning to Quickly Identify Buildings that Waste Electricity