New software developed at the U.S. Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL) can inexpensively produce calibrated energy models of buildings.
Available on GitHub as open source code, the Autotune Code can tune residential and commercial building energy efficiency models to match measured data. The software can help reduce the amount of time and expertise that is needed to optimize building parameters for cost and energy savings.
Autotune enables "no-touch" audits, optimal retrofits, and other simulation-informed applications to be cost-effectively realized for buildings, which are traditionally too small to be serviced by the industry. "The methodology uses multi-parameter optimization techniques, in combination with big data mining-informed artificial intelligence agents, to automatically modify software inputs so simulation output matches measured data," says ORNL researcher Joshua New.
The team used DOE resources such as the Titan supercomputer to perform simulations. The code contains a backend that performs the evolutionary calibration, a Web service that allows scripting for calibrating large numbers of buildings, and a front end website that enables users to interact with the software.
"Instead of having a human change the knobs, so to speak, the Autotune methodology does that for you," says ORNL researcher Jibonananda Sanyal.
From Oak Ridge National Laboratory
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