Influencing Factors for an Integrated Model of Green Building Energy Consumption Using BIM Dynamic Simulation and Multiobjective Decision-Making

Abstract

Multiobjective decision-making, also known as multiobjective optimization, indicates that the needs of numerous goals impact the system scheme’s selection and decision-making direction. The rapid economic development and the acceleration of urbanization have promoted the prosperity of the construction industry, but at the same time, the problem of building energy consumption is also becoming more and more serious. In order to overcome this issue, this study proposes an optimization method for the impact selection of green building energy consumption. The machine learning (ML) algorithm, random forest (RF) is used to discover and design criteria relating to the influence of green building energy consumption. Then, based on the data of building information modeling (BIM) dynamic simulation, the optimization of the RF model in multiobjective decision-making in green building energy consumption is presented. Furthermore, a comparative study of the proposed system with the existing systems and deep learning (DL) models is also conducted. Performance of the proposed system is measured in terms of accuracy and ROC curve. The proposed system achieved a training accuracy of 95% and a testing accuracy of 83% which is superior to the earlier approaches. The experimental results show that the RF algorithm can effectively determine the relationship between the influencing factors of green building energy consumption. This approach also enables policymakers to better understand the complex relationships between green building energy consumption, which may subsequently improve the acceptability of decision-making.