Building a top-down method based on machine learning for evaluating energy intensity at a fine scale

doi:10.1016/j.energy.2022.124505

	Building a top-down method based on machine learning for evaluating energy intensity at a fine scale
	Guo, Jinyu 2,3; Ma, Jinji 2,3; Li, Zhengqiang 1; Hong, Jin 4
刊名	ENERGY
	2022-09-15
卷号	255
关键词	Energy efficiency Energy intensity Machine learning Multi-source satellite data Top-down Fine-scale
ISSN号	0360-5442
DOI	10.1016/j.energy.2022.124505
通讯作者	Ma, Jinji(jinjima@ahnu.edu.cn)
英文摘要	Energy intensity is an important representative of energy efficiency. Currently, most countries lack finescale energy intensity data, taking China as an example, it only published provincial energy intensity data. However, the published large-scale energy intensity cannot support the formulation of local policies. What's more, the research work about the evaluation of fine-scale energy intensity is rare. To solve this problem, a "top-down" method based on machine learning is proposed to evaluate the fine-scale energy intensity. Appropriate features were extracted from multi-source satellite data, then the performances of multiple machine learning models were compared. It is found that deep neural network reaches the highest level among these models. Therefore, it was selected to estimate city-scale energy intensity from the year of 2001-2017. It turns out that the energy efficiency of southeast cities is higher than that of northwest cities in China, and most cities are developing towards the direction of improving energy efficiency. Among all cities, the central ones are the fastest to improve energy efficiency. However, the energy efficiency of a few cities is found to reduce during this period. The proposed method can also be used in other countries to help governments save energy and reduce emissions. (c) 2022 Elsevier Ltd. All rights reserved.
资助项目	National Natural Science Foundation of China[41671352] ; top notch university[gxbjZD06] ; K. C. Wong Education Foundation[GJTD-2018-15]
WOS关键词	INDUSTRIAL-STRUCTURE
WOS研究方向	Thermodynamics ; Energy & Fuels
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000862250400009
资助机构	National Natural Science Foundation of China ; top notch university ; K. C. Wong Education Foundation
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/129117]
专题	中国科学院合肥物质科学研究院
通讯作者	Ma, Jinji
作者单位	1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Environm Protect Key Lab Satellite Remote Se, Beijing 100101, Peoples R China 2.Anhui Normal Univ, Sch Geog & Tourism, Wuhu 241003, Peoples R China 3.Engn Technol Res Ctr Resources Environm & GIS, Wuhu 241003, Anhui, Peoples R China 4.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Opt Calibrat & Characterizat, Hefei 230031, Peoples R China
推荐引用方式 GB/T 7714	Guo, Jinyu,Ma, Jinji,Li, Zhengqiang,et al. Building a top-down method based on machine learning for evaluating energy intensity at a fine scale[J]. ENERGY,2022,255.
APA	Guo, Jinyu,Ma, Jinji,Li, Zhengqiang,&Hong, Jin.(2022).Building a top-down method based on machine learning for evaluating energy intensity at a fine scale.ENERGY,255.
MLA	Guo, Jinyu,et al."Building a top-down method based on machine learning for evaluating energy intensity at a fine scale".ENERGY 255(2022).