Constructing large scale surrogate models from big data and artificial intelligence

EnergyPlus is the U.S. Department of Energy’s flagship 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 2–3min 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-min 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 3s, greatly reducing the required simulation runtime for relatively close results. The techniques shown here allow any software to be approximated by machine learning in a way that allows one to quantify the trade-off of accuracy for execution time.