Building information modelling-enabled multi-objective optimization for energy consumption parametric analysis in green buildings design using hybrid machine learning algorithms

Green buildings (GB) have been widely promoted in various nations. However, the post occupancy evaluation suggests many GB cannot well fulfill the expected targets. To overcome the mismatch among GB's multiobjectives, this paper develops an efficient and intelligent hybrid method using BIM-DesignBuilder (BIM-DB), Grey wolf optimization (GWO), random forest (RF) and non-dominated sorting genetic algorithm II (NSGA-II) to achieve the optimization of design parameters. The BIM model and DB simulation tool were used to obtain data samples of envelope and air conditioning system design parameters, and their life cycle carbon emission (LCCE), economic cost (EC) and predicted mean vote (PMV). The RF model was used to achieve high precision prediction. The GWO was used in the hyper-parameter optimization. The NSGA-II algorithm was applied to multi-objective optimization to obtain optimal design parameters. A building case shows: (1) The RF model had an excellent prediction performance for LCCE, EC and PMV. (2) BIM-DB can be used to obtain low error and high reliability building simulation data sets. (3) The RF-NSGA-II intelligent algorithm can reduce the LCCE of the building in the entire cycle by 16.6%, reduce the EC per square meter by 2.0%, and greatly improve the thermal comfort by 18.3%, representing good application value. This research provides a way of thinking for the multiobjective optimization of green buildings from the perspective of data mining and guidance for the parameter selection of the envelopes and air conditioning systems of new and existing buildings to more scientifically and effectively design green buildings.