Research Article | OPEN ACCESS
AR-based Algorithms for Short Term Load Forecast
Zuhairi Baharudin, Mohd. Azman Zakariya, Mohd. HarisMdKhir, Perumal Nallagownden and Muhammad Qamar Raza
Electrical and Electronics Department, Universiti Teknologi PETRONAS, 31750 Tronoh, Perak, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2014 6:1223-1229
Received: March 28, 2013 | Accepted: April 15, 2013 | Published: February 15, 2014
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.
Keywords:
Artificial neural network, Autoregressive (AR), linear predictor, Short Term Load Forecast (STLF),
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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