The US Department of Energy’s Oak Ridge National Laboratory (ORNL) has developed Autotune, a set of automated calibration techniques for tuning residential and commercial building energy efficiency software models to match measured data.
The software is now available as open source code on GitHub.
The automated calibration software reduces the amount of time and expertise needed to optimize building parameters for cost and energy savings.
By cheaply producing calibrated building energy models, Autotune allows “no-touch” audits, optimal retrofits and other simulation-informed applications to be cost-effectively realized for buildings traditionally too small (below 50,000 square feet) to be serviced by industry.
To develop the software, a team of ORNL researchers used DOE supercomputing and computational resources — including ORNL’s Titan supercomputer and the National Institute for Computational Sciences’ Nautilus system — to perform millions of EnergyPlus simulations for a range of standard building types with generated data totaling hundreds of terabytes.
By mining this data and trying many different calibration algorithms, the researchers were able to identify techniques and evolutionary computations that can quickly evolve a building to match measured data. On Titan, the team has been able to run annual energy simulations for more than half a million buildings and write 45 terabytes of simulation output to disk in less than one hour using over a third of Titan’s nearly 300,000 CPU cores in parallel.
The open-source code contains the following:
- A backend that performs the evolutionary calibration.
- A web service that allows scripting for calibrating large numbers of buildings.
- A front end website which allows users to interact with the software.