A hybrid forecasting model to improve cost prediction accuracy in green building projects with machine learning

Purpose – The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors. Design/methodology/approach – This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction. Findings – The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R2 = 0.319) and superior prediction accuracy for different cost terms. Originality/value – Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.