This presentation, “Exploration of Neural Networks and Kalman Filters to Estimate Parameters Driving Energy Use,” was created by Suhas Raju (Ph.D. student in electrical engineering, UNC Charlotte) as part of the Duke University Energy Data Analytics Ph.D. Student Fellowship Program.

Abstract: “The buildings sector accounts for approximately 33% of worldwide greenhouse gas emissions and more than 40% of primary energy usage. Over the past 3 years we have analyzed over 6,000 small commercial buildings and identified issues driving high energy usage. Achieving only a 1% reduction in each of those 6,000 facilities would represent the removal of about 380 residential homes from the grid. The work in this project will focus on developing appropriate artificial intelligence approaches to leverage this large data set to detect reasons for high energy consumption.”

Support for this work was provided by the Alfred P. Sloan Foundation Grant G-2020-13922 through the Duke University Energy Data Analytics Ph.D. Student Fellowship.

Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff.

Learn about the Energy Data Analytics Lab at Duke: energy.duke.edu/research/energy-data
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