TY  - JOUR
EP  - 1043
ID  - scholars5482
IS  - 6
TI  - AR-based algorithms for short term load forecast
N2  - 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.
SN  - 20407459
AV  - none
SP  - 1037
PB  - Maxwell Science Publications
A1  - Baharudin, Z.
A1  - Azman Zakariya, M.
A1  - HarisMdKhir, M.
N1  - cited By 4
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893356756&doi=10.19026%2frjaset.7.384&partnerID=40&md5=c90c06570131e567ba4e81713d4579c7
Y1  - 2014///
VL  - 7
JF  - Research Journal of Applied Sciences, Engineering and Technology
ER  -