Comparative study of metamodelling techniques in building energy simulation: Guidelines for practitioners

In this paper, a strategy that is both reliable and time-efficient is provided in order to guide users in their metamodelling problems. Furthermore, polynomial regression (PR), multivariate adaptive regression splines (MARS), kriging (KR), radial basis function networks (RBF), and neural networks (NN) are compared on a building energy simulation problem. We find that for the outputs of this example and based on Root Mean Squared Error (RMSE), coefficient of determination (R2), and Maximal Absolute Error (MAE), KR and NN are the overall best techniques. Although MARS perform slightly worse than KR and NN, it is preferred because of its simplicity. For different applications, other techniques might be optimal.