Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case

Der-Chiang Li, Che-Jung Chang, Chien-Chih Chen and Wen-Chih Chen

Omega, 2012, vol. 40, issue 6, 767-773

Abstract: The overall electricity consumption, treated as a primary guideline for electricity system planning, is a major measurement to indicate the degree of a nation's development. The electricity consumption forecast is especially important with regard to policy making in developing countries (Asian countries in this work). However, since the economic growth rates in these countries are usually high and unstable, it is difficult to obtain accurate predictions using long-term data, and thus forecasting with limited (short-term) data is more effective and of considerable interest. Grey theory is one approach that can be used to construct a model with limited samples to provide better forecasting advantage for short-term problems. The forecasting performance of AGM(1,1), based on grey theory, has been confirmed using the Asia-Pacific economic cooperation energy database, and the results, compared with those obtained from back propagation neural networks (BPN) and support vector regression (SVR), show that the proposed approach can effectively deal with the problem of forecasting electricity consumption when the sample size is limited.

Keywords: Forecasting; Grey theory; Electricity consumption; Small data set (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (45)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0305048311001423
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:jomega:v:40:y:2012:i:6:p:767-773

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.omega.2011.07.007

Access Statistics for this article

Omega is currently edited by B. Lev

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