A comparison of grid-level residential electricity demand scenarios in Japan for 2050

Yoshiki Yamagata, Daisuke Murakami and Hajime Seya

Applied Energy, 2015, vol. 158, issue C, 255-262

Abstract: Scenarios of future population at the grid level (e.g., 1km2) is very important for various types of regional planning, including energy planning. Though the most often used method for population projection is the so-called cohort-component method, if our focus is world level, not country or finer level, we must rely on much simpler methods. In this study, we compare the performance of several typical simple population projection methods using the residential electricity demand derived from each population scenario, especially focusing on spatial patterns. We show that different projection methods may produce different spatial patterns such as “compact” or “dispersed” urban form, which is quantified using the Gini coefficient. Also, we show that the projection method for gravity-based residential electricity demand is very flexible in that it can create the most compact and the most dispersed urban form among the focused methods. Furthermore, we point out the usefulness of model ensemble in projection.

Keywords: Scenarios; Population; Residential electricity demand; Grid level; Comparison; Model ensemble (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261915010156
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:158:y:2015:i:c:p:255-262

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2015.08.079

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().