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2014 (Vol. 7, Issue: 6)
Article Information:

AR-based Algorithms for Short Term Load Forecast

Zuhairi Baharudin, Mohd. Azman Zakariya, Mohd. HarisMdKhir, Perumal Nallagownden and Muhammad Qamar Raza
Corresponding Author:  Zuhairi Baharudin 

Key words:  Artificial neural network, Autoregressive (AR), linear predictor, Short Term Load Forecast (STLF) , , ,
Vol. 7 , (6): 1223-1229
Submitted Accepted Published
March 28, 2013 April 15, 2013 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.
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  Cite this Reference:
Zuhairi Baharudin, Mohd. Azman Zakariya, Mohd. HarisMdKhir, Perumal Nallagownden and Muhammad Qamar Raza, 2014. AR-based Algorithms for Short Term Load Forecast.  Research Journal of Applied Sciences, Engineering and Technology, 7(6): 1223-1229.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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