Sensitivity Analysis of Building Energy Performance Assessment Based on Machine-Learning Models

2020 ◽  
pp. 401-409
Author(s):  
Wei Tian ◽  
Jiaxin Shi ◽  
Pieter de Wilde ◽  
Yu Sun ◽  
Chuanqi Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Performance Assessment ◽  
Building Energy ◽  
Energy Performance ◽  
Learning Models ◽  
Building Energy Performance ◽  
Machine Learning Models

ENERGY ASSESSMENT OF URBAN BUILDINGS BASED ON GEOGRAPHIC INFORMATION SYSTEM

2020 ◽  
Vol 15 (3) ◽  
pp. 83-93
Author(s):  
Wei Tian ◽  
Chuanqi Zhu ◽  
Yunliang Liu ◽  
Baoquan Yin ◽  
Jiaxin Shi
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Energy Saving ◽  
Energy Analysis ◽  
Building Energy ◽  
Learning Models ◽  
Energy Assessment ◽  
Urban Buildings ◽  
Energy Models ◽  
Machine Learning Models

ABSTRACT Urban building energy analysis has attracted more attention as the population living in cities increases as does the associated energy consumption in urban environments. This paper proposes a systematic bottom-up method to conduct energy analysis and assess energy saving potentials by combining dynamic engineering-based energy models, machine learning models, and global sensitivity analysis within the GIS (Geographic Information System) environment for large-scale urban buildings. This method includes five steps: database construction of building parameters, automation of creating building models at the GIS environment, construction of machine learning models for building energy assessment, sensitivity analysis for choosing energy saving measures, and GIS visual evaluation of energy saving schemes. Campus buildings in Tianjin (China) are used as a case study to demonstrate the application of the method proposed in this research. The results indicate that the method proposed here can provide reliable and fast analysis to evaluate the energy performance of urban buildings and determine effective energy saving measures to reduce energy consumption of urban buildings. Moreover, the GIS-based analysis is very useful to both create energy models of buildings and display energy analysis results for urban buildings.

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