Design Optimization of a Passive Building with Green Roof through Machine Learning and Group Intelligent Algorithm
Yaolin Lin, Luqi Zhao, Xiaohong Liu, Wei Yang, Xiaoli Hao, Lin Tian
BUILDINGS | MDPI | Published : 2021
This paper proposed an optimization method to minimize the building energy consumption and visual discomfort for a passive building in Shanghai, China. A total of 35 design parameters relating to building form, envelope properties, thermostat settings, and green roof configurations were considered. First, the Latin hypercube sampling method (LHSM) was used to generate a set of design samples, and the energy consumption and visual discomfort of the samples were obtained through computer simulation and calculation. Second, four machine learning prediction models, including stepwise linear regression (SLR), back-propagation neural networks (BPNN), support vector machine (SVM), and random forest..View full abstract
Awarded by Natural Science Foundation of Hubei Province
Awarded by Hunan Provincial Department of housing and urban rural development
This research was funded by Natural Science Foundation of Hubei Province, grant number [2017CFB602] and Hunan Provincial Department of housing and urban rural development, grant number [KY2016063].