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