Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm

Abstract

The realization of a near zero energy consumption building (NZEB) is one of the important goals to promote the sustainable development of green buildings. To achieve the goal of NZEB and to allow for factors such as building comfort, environmental protection and cost, an intelligent optimization framework that combines Building Information Modeling DesignBuilder(BIM-DB) and random forest-nondominated sorting genetic algorithm-III (RF-NSGA-III) is proposed to study the multiobjective optimization of NZEB performance. The data in this paper are simulated by BIM-DB through orthogonal experimental design. RF is used to establish the complex relationship between building design parameters and NZEB performance. After the accuracy of the model is verified by the goodness of fit (R2) and the root mean square error (RMSE), the obtained nonlinear mapping relationship is taken as the fitness function of NSGA-III, and the multiobjective Pareto optimal solution set is obtained by using the proposed RF-NSGA-III intelligent optimization framework. Taking a teaching building in Wuhan as an example, the effectiveness of this method is verified. The results show that (1) the RF prediction accuracy is good, the RMSE range for the performance of NZEB is 0.015–0.035, and the R2 range is 0.91–0.93. (2) After optimization, the energy-saving rate of the building body is 21.25%, which meets the near zero energy consumption standard. (3) The proposed RF-NSGA-III intelligent optimization framework can achieve multiobjective optimization, and the comprehensive optimization rate of five objectives is the best compared with three and four objectives. Accordingly, this intelligent method that combines BIM-DB and RF-NSGA-III provides an effective idea for the design optimization of NZEB.