Investigation into the Hyperparameters of Error-Based Adaptive Sampling Approach for Surrogate Modeling - Leonid Legashev; Sergey Tolmachev; Irina Bolodurina; Alexander Shukhman; Lyubov Grishina (Modelling)
Surrogate modeling technology is used to create lightweight analogs of resource- and calculation-intensive software, provided that the problem can be reduced to the regression problem. In this article, we construct a surrogate model for predicting annual energy consumption using the open-source EnergyPlus software and various sampling techniques. A general algorithm for an error-based adaptive sampling technique to build the surrogate model is presented. The best results were shown by the composite Mixed Sampling method with a data refining window the size of 70% and a LightGBM regression model. The best attained metrics values are as follows: MSE = 7.76, RMSE = 1.47, MAE = 0.98 and R2 = 0.99. For a small number of iterations, an error-based adaptive sampling technique with hyperparameter tuning is preferable to the static sampling approach. For a large number of iterations, both techniques show approximately good predictive results of the built surrogate model. After hyperparameter tuning was performed, the average value of the MSE metric decreased from 43.43 to 7.76. A gas thickness feature greater than 0.015 had no positive effect on energy-saving optimization. For temperatures on a summer day of 30 degrees and above, there was a sharp increase in energy consumption. The maximum dry bulb temperature on a winter and summer day and the wind speed on a winter day were the most important features of the built surrogate model with 492, 483 and 443 gain values of the feature importance method, respectively.