Passive Optimization Design Based on Particle Swarm Optimization in Rural Buildings of the Hot Summer and Warm Winter Zone of China

Sustainability

Shaoqun Zheng¹  Ran Wang¹  Shilei Lu¹ 

[1] School of Environment Science and Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, China;

关键词: rural residence;    green building;    energy consumption;    multidimensional optimization;    particle swarm optimization;    regression analysis;   

DOI  :  10.3390/su9122288

来源: DOAJ

【 摘 要 】

The development of green building is an important way to solve the environmental problems of China’s construction industry. Energy conservation and energy utilization are important for the green building evaluation criteria (GBEC). The objective of this study is to evaluate the quantitative relationship between building shape parameter, envelope parameters, shading system, courtyard and the energy consumption (EC) as well as the impact on indoor thermal comfort of rural residential buildings in the hot summer and warm winter zone (HWWZ). Taking Quanzhou (Fujian Province of China) as an example, based on the field investigation, EnergyPlus is used to build the building performance model. In addition, the classical particle swarm optimization algorithm in GenOpt software is used to optimize the various factors affecting the EC. Single-objective optimization has provided guidance to the multi-dimensional optimization and regression analysis is used to find the effects of a single input variable on an output variable. Results shows that the energy saving rate of an optimized rural residence is about 26–30% corresponding to the existing rural residence. Moreover, the payback period is about 20 years. A simple case study is used to demonstrate the accuracy of the proposed optimization analysis. The optimization can be used to guide the design of new rural construction in the area and the energy saving transformation of the existing rural houses, which can help to achieve the purpose of energy saving and comfort.

【 授权许可】

Unknown