Electricity consumption prediction using a neural-network-based grey forecasting approach

Yi-Chung Hu ()
Additional contact information
Yi-Chung Hu: Fujian Agriculture and Forestry University

Journal of the Operational Research Society, 2017, vol. 68, issue 10, 1259-1264

Abstract: Abstract Electricity consumption is an important economic index and plays a significant role in drawing up an energy development policy for each country. Multivariate techniques and time-series analysis have been proposed to deal with electricity consumption forecasting, but a large amount of historical data is required to obtain accurate predictions. The grey forecasting model attracted researchers by its ability to characterize an uncertain system effectively with a limited number of samples. GM(1,1) is the most frequently used grey forecasting model, but its developing coefficient and control variable were dependent on the background value that is not easy to be determined, whereas a neural-network-based GM(1,1) model called NNGM(1,1) has been presented to resolve this troublesome problem. This study has applied NNGM(1,1) to electricity consumption and has examined its forecasting ability on electricity consumption using sample data from the Turkish Ministry of Energy and Natural Resources and the Asia–Pacific Economic Cooperation energy database. Experimental results demonstrate that NNGM(1,1) performs well.

Keywords: electricity consumption; neural network; grey forecasting; GM(1; 1) (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

Downloads: (external link)
http://link.springer.com/10.1057/s41274-016-0150-y Abstract (text/html)
Access to full text is restricted to subscribers.

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:pal:jorsoc:v:68:y:2017:i:10:d:10.1057_s41274-016-0150-y

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

DOI: 10.1057/s41274-016-0150-y

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().