@article{scholars5482,
            year = {2014},
         journal = {Research Journal of Applied Sciences, Engineering and Technology},
       publisher = {Maxwell Science Publications},
           pages = {1037--1043},
          volume = {7},
            note = {cited By 4},
          number = {6},
             doi = {10.19026/rjaset.7.384},
           title = {AR-based algorithms for short term load forecast},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893356756&doi=10.19026\%2frjaset.7.384&partnerID=40&md5=c90c06570131e567ba4e81713d4579c7},
        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. {\^A}{\copyright} Maxwell Scientific Organization, 2014.},
            issn = {20407459},
          author = {Baharudin, Z. and Azman Zakariya, M. and HarisMdKhir, M.}
}