Abstract
EnergyPlus is the U.S. Department of Energy's
agship whole-building energy
simulation engine and provides extensive simulation capabilities. However, the
computational cost of these capabilities has resulted in annual building simulations
that typically requires 2{3 minutes of wall-clock time to complete. While
EnergyPlus's overall speed is improving (EnergyPlus 7.0 is 25{40% faster than
EnergyPlus 6.0), the overall computational burden still remains and is the top
user complaint. In other engineering domains, researchers substitute surrogate
or approximate models for the computationally expensive simulations to improve
simulation and reduce calibration time. Previous work has successfully
demonstrated small-scale EnergyPlus surrogate models that use 10{16 input
variables to estimate a single output variable. This work leverages feed forward
neural networks and Lasso regression to construct robust large-scale EnergyPlus
surrogate models based on 3 benchmark datasets that have 7{156 inputs. These
models were able to predict 15-minute values for most of the 80{90 simulation
outputs deemed most important by domain experts within 5% (whole building
energy within 0.07%) and calculate those results within 3 seconds, greatly
reducing the required simulation runtime for relatively close results.
