A new hybrid day-ahead peak load forecasting method for Iran’s National Grid

M. Moazzami, A. Khodabakhshian and R. Hooshmand

Applied Energy, 2013, vol. 101, issue C, 489-501

Abstract: This paper presents a new hybrid forecasting engine for day-ahead peak load prediction in Iran National Grid (ING). In this forecasting engine the seasonal data bases of the historical peak load demand on the similar days with their weather information given for three cities (Tehran, Tabriz and Ahvaz) have been used. Wavelet decomposition is used to capture low and high frequency components of each data base from original noisy signals. A separate ANN with an iterative training mechanism which is optimized by genetic algorithm is employed for each low and high frequency data base. A day-ahead peak demand is determined with the reconstruction of low and high frequency output components of each ANN. Simulation results show the effectiveness and the superiority of the proposed strategy when compared with other methods for daily peak load demand forecasting in ING and EUNITE test cases.

Keywords: Peak Load Forecasting (PLF); Wavelet decomposition; Artificial Neural Network (ANN); Genetic optimization; Iran’s National Grid (ING) (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261912004515
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:101:y:2013:i:c:p:489-501

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.2012.06.009

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 ().