全球智库信息集成服务系统(G2TT): Downscaled energy demand projection at the local level using the Iterative Proportional Fitting procedure.

G2TT

来源类型	Article
规范类型	其他
DOI	10.1016/j.apenergy.2019.01.051
	Downscaled energy demand projection at the local level using the Iterative Proportional Fitting procedure.
	Ahn Y-H; Woo J-H; Yoo SJ
发表日期	2019
出处	Applied Energy 238: 384-400
出版年	2019
语种	英语
摘要	In this paper we forecast energy demand at the local level using a new two-dimensional downscaling methodology that respects regional and subnational variation and, as well as consistency at the aggregate level. This methodology combines the one-dimensional statistical downscaling and the Iterative Proportional Fitting (IPF) procedure. Previous projection studies based on downscaling methods have focused on allocating one-dimensional quantity indicators such as GDP, population, and emissions, into geographically or administratively lower levels. In case of energy demand projections, however, downscaling involves not only regional allocation of energy demand quantities but also energy mixes of the demands in the regions. Our novel two-dimensional downscaling methodology allows us to downscale national energy projections in both demand quantities and their energy mix to regional levels by combining the statistical downscaling and the IPF procedure. We illustrate the methodology by deriving energy demand projections for provinces and metropolitan cities in Korea from national scenarios. In addition, we provide two more simple applications of the IPF to show utilization potentials of the IPF in energy demand forecasting and statistics.
主题	Air Quality & ; Greenhouse Gases (AIR)
关键词	Downscaling Energy demand projection at the local level GAINS-Korea Iterative Proportional Fitting procedure
URL	http://pure.iiasa.ac.at/id/eprint/15718/
来源智库	International Institute for Applied Systems Analysis (Austria)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/131691
推荐引用方式 GB/T 7714	Ahn Y-H,Woo J-H,Yoo SJ. Downscaled energy demand projection at the local level using the Iterative Proportional Fitting procedure.. 2019.