eprintid: 5482
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/54/82
datestamp: 2023-11-09 16:17:13
lastmod: 2023-11-09 16:17:13
status_changed: 2023-11-09 16:01:49
type: article
metadata_visibility: show
creators_name: Baharudin, Z.
creators_name: Azman Zakariya, M.
creators_name: HarisMdKhir, M.
title: AR-based algorithms for short term load forecast
ispublished: pub
note: cited By 4
abstract: Short-term load forecast plays an important role in planning and operation of power systems. The accuracy of the forecast value is necessary for economically efficient operation and effective control of the plant. This study describes the methods of Autoregressive (AR) Burg's and Modified Covariance (MCOV) in solving the short term load forecast. Both algorithms are tested with power load data from Malaysian grid and New South Wales, Australia. The forecast accuracy is assessed in terms of their errors. For the comparison the algorithms are tested and benchmark with the previous successful proposed methods. © Maxwell Scientific Organization, 2014.
date: 2014
publisher: Maxwell Science Publications
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893356756&doi=10.19026%2frjaset.7.384&partnerID=40&md5=c90c06570131e567ba4e81713d4579c7
id_number: 10.19026/rjaset.7.384
full_text_status: none
publication: Research Journal of Applied Sciences, Engineering and Technology
volume: 7
number: 6
pagerange: 1037-1043
refereed: TRUE
issn: 20407459
citation:   Baharudin, Z. and Azman Zakariya, M. and HarisMdKhir, M.  (2014) AR-based algorithms for short term load forecast.  Research Journal of Applied Sciences, Engineering and Technology, 7 (6).  pp. 1037-1043.  ISSN 20407459