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In this paper, we proposed an integrated optimization framework to explore minimum building carbon emissions (BCE), indoor discomfort hours (IDH), and global cost (GC) of building, as well as a new formula for selecting the best scheme in the Pareto front set. Such framework improves the efficiency of the optimization process, the accuracy of the results, and the rationality of the best scheme. The entire optimization process can be divided into four steps. First, the input parameters were randomly generated using three sampling methods and then simulated to build the database. Then, the contribution rates of the selected parameters to the outputs were comprehensively evaluated and combined with multi-sensitivity analysis methods to screen important parameters. Next, we trained and validated the Back Propagation Neural Network (BPNN) model, in which different methods were used for hyperparametric optimization. Third, based on the comparison of various optimization methods, the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) was selected and combined with BPNN to solve the proposed multi-objective optimization problem. Then, finally, we applied the proposed optimal balance formula to select the scheme that considered all aspects of objectives in the Pareto front set. The results demonstrated that the best sampling method and hyperparameter combination can result in an R2 of BPNN that reaches 0.992. The simulation results are in good agreement with the optimization results. Compared with the case building, the optimal balance schemes of BCE, IDH, and GC were reduced by 53.25%, 42.95%, and 22.33%, respectively. Therefore, we demonstrated that this method is feasible and effective for improving building design in more practical and complex situations and can be widely popularized in the building performance optimization field.
Chen et al. (Sun,) studied this question.