气候变化领域集成服务门户(CCPortal): Enhancing typical Meteorological Year generation for diverse energy systems: A hybrid Sandia-machine learning approach

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DOI	10.1016/j.renene.2024.120369
	Enhancing typical Meteorological Year generation for diverse energy systems: A hybrid Sandia-machine learning approach
	Zhang, Wenhao; Li, Honglian; Wang, Mengli; Lv, Wen; Huang, Jin; Yang, Liu
发表日期	2024
ISSN	0960-1481
EISSN	1879-0682
起始页码	225
卷号	225
英文摘要	Accurate performance assessment of energy systems heavily relies on Typical Meteorological Year (TMY) data. The Sandia method, commonly used for TMY generation, is limited by default weighting criteria for meteorological parameters, restricting its suitability for diverse energy system analyses. In response, this study presents a novel framework for generating TMY files customized to various energy systems. Utilizing three tree-based algorithms (CART, RF, XGBoost) and interpretable machine learning techniques, the framework quantifies and personalizes weighting schemes. Validation of the method's applicability is conducted using long-term historical weather data from Beijing and Lhasa, encompassing three distinct energy systems (a full air conditioning building system and two renewable energy systems). Results indicate that the new TMY generation method excels over the original Sandia method for building and photovoltaic systems but encounters limitations with wind power system. Additionally, incorporating meteorological parameters highly relevant to specific energy systems and comprehensively considering their seasonality will contribute to the development of more representative TMY data. The proposed method facilitates precise foundational climate data acquisition, enabling more accurate energy performance analysis and decision-making.
英文关键词	Typical meteorological year; Sandia method; Renewable energy; Weighting factors; Feature importance; Seasonal patterns
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Energy & Fuels
WOS类目	Green & Sustainable Science & Technology ; Energy & Fuels
WOS记录号	WOS:001220494200001
来源期刊	RENEWABLE ENERGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/294768
作者单位	Xi'an University of Architecture & Technology; Xi'an University of Architecture & Technology; Xi'an University of Architecture & Technology
推荐引用方式 GB/T 7714	Zhang, Wenhao,Li, Honglian,Wang, Mengli,et al. Enhancing typical Meteorological Year generation for diverse energy systems: A hybrid Sandia-machine learning approach[J],2024,225.
APA	Zhang, Wenhao,Li, Honglian,Wang, Mengli,Lv, Wen,Huang, Jin,&Yang, Liu.(2024).Enhancing typical Meteorological Year generation for diverse energy systems: A hybrid Sandia-machine learning approach.RENEWABLE ENERGY,225.
MLA	Zhang, Wenhao,et al."Enhancing typical Meteorological Year generation for diverse energy systems: A hybrid Sandia-machine learning approach".RENEWABLE ENERGY 225(2024).