Study on energy consumption, thermal comfort and economy of passive buildings based on multi-objective optimization algorithm for existing passive buildings

The rapid development of urbanization in China inevitably leads to a sharp increase in building energy con-sumption, and Passive House (PH) have received much attention in China in recent years. In this paper, the annual energy demand (AED) and summer discomfort hours (SUH) of PH are used as optimization objectives to establish a set of design solutions for multi-objective optimization (MTO) and economic analysis of a PH in Shandong Province, China. It is found that (1) the algorithm based on nearest neighbor component analysis (NCA) feature extraction applies to the sensitivity analysis of multivariate nonlinear high-dimensional data, and the NCA-GA-RBF neural network technique is proposed, which has significantly improved the goodness of fit and generalization ability for AED and SUH. (2) The NSGA-II genetic algorithm is used for the optimization search, and the results show that the overall improvement rates of AED and SUH are 25.5% and 21.6%, respectively, among the nine solutions after comprehensive evaluation; (3) Finally, the optimal scheme of energy saving and the optimal scheme of the economy are selected for economic evaluation. By comparing the economics of the two options and the improvement rates of AEDs and SUHs, the guidelines for design decision-makers are provided. In summary, the design scheme of this paper is forward-looking and reference-oriented and provides some reference suggestions for the development of PH in China.

Automated summary

This paper investigates the multi-objective optimization and economic analysis of Passive House (PH) in Shandong Province, China. By utilizing the NCA-GA-RBF neural network technique and the NSGA-II genetic algorithm, significant improvements in annual energy demand (AED) and summer discomfort hours (SUH) are achieved. The optimal energy-saving and economic schemes are selected for economic evaluation, providing guidance for design decision-makers.