A machine learning-based surrogate model to approximate optimal building retrofit solutions

doi:10.1016/j.apenergy.2020.116024

GSTDTAP > 资源环境科学

DOI	10.1016/j.apenergy.2020.116024
	A machine learning-based surrogate model to approximate optimal building retrofit solutions
	Emmanouil Thrampoulidis, Georgios Mavromatidis, Aurélien Lucchi and Kristina Orehounig
	2020
出版年	2020
国家	瑞士
领域	资源环境
英文摘要	The building sector has the highest share of operational energy consumption and greenhouse gas emissions among all sectors. Environmental targets set by many countries impose the need to improve the environmental footprint of the existing building stock. Building retrofit is considered one of the most promising solutions to-wards this direction. In this paper, a surrogate model for evaluating the necessary building envelope and energy system measures for building retrofit is presented. Artificial neural networks are exploited to build up this model in order to provide a good balance between accuracy and computational cost. The proposed model is trained and tested for the case study of the city of Zurich, in Switzerland, and is compared with one of the most advanced models for building retrofit that uses building simulation and optimization tools. The surrogate model operates on a smaller input set and the time required to derive retrofit solutions is reduced from 3.5 min to 16.4 μsec. Results show that the proposed model can provide significantly reduced computational cost without compro-mising accuracy for most of the retrofit dimensions. For instance, the retrofit costs and the energy system se-lections are approximated with an average accuracy of R2=0.9408 and f1score=0.9450, respectively. Finally, yet importantly, such surrogate retrofit models may effectively be used for bottom-up retrofit analyses for wide areas and can contribute towards accelerating the adoption of retrofit measures.
URL	查看原文
来源平台	Centre for Energy Policy and Economics
引用统计	被引频次：43[WOS] [WOS记录] [WOS相关记录]
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/342450
专题	资源环境科学
推荐引用方式 GB/T 7714	Emmanouil Thrampoulidis, Georgios Mavromatidis, Aurélien Lucchi and Kristina Orehounig. A machine learning-based surrogate model to approximate optimal building retrofit solutions,2020.