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id caadria2019_245
authors Jiaxin, Zhang, Yunqin, Li, Haiqing, Li and Xueqiang, Wang
year 2019
title Sensitivity Analysis of Thermal Performance of Granary Building based on Machine Learning
source M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 665-674
summary The granary building form has significant effects on thermal performance, especially in hot climate regions. This research is focused on exploring the influences of parameters relevant to building form design on thermal performance for granary buildings in Jiangsu and Anhui, China(both provinces belong to the hot summer region). The usual method is to use simulation software to perform a sensitivity analysis of thermal performance to assess the impacts of granary design parameters and identify the essential characteristics. However, many factors are affecting the thermal performance of granary buildings. The use of traditional energy simulation software requires calculation and analysis of a large number of models. In this study, we build a machine learning model to predict the thermal performance of granary buildings and identify the most influential design parameters of thermal performance in granary building. The input parameters include outdoor temperature, building height, aspect ratio, orientation, heat transmission coefficient of the wall and roof, and overall scale. The results show that the overall building scale is the most influential variable to the annual electricity consumption for cooling, whereas the heat transmission coefficient of the roof is the most influential to the change of the indoor temperature.
keywords Sensitivity analysis; Artificial Neural Networks (ANNs); Thermal performance; Granary building
series CAADRIA
full text file.pdf (4,684,870 bytes)
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