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    Short Term Load Forecasting using Functional Link Network

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    Date
    2011-04
    Author
    Muthukumaran, Sithambaram
    Thyagarajah, K.
    Paulraj, Murugesa Pandiyan, Prof. Madya Dr.
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    Abstract
    Short Term Load Forecasting (STLF) is an important tool for successful planning and operation of power generating stations. This paper proposes neural network algorithm for STLF using Functional Link Neural Network (FLN) with Slope Parameter. This Neural Network Algorithm includes peak and minimum loads and load factors as additional inputs for the final forecast. The Proposed Functional Link Network with Slope Parameter (FLNSP) is tested for the Tamilnadu state, (India) grid data and the results are compared with the Conventional Back Propagation (CBP) method. Simulation results indicate that the proposed forecasting techniques are effective.
    URI
    http://www.eurojournals.com/EJSR_51_3_03.pdf
    http://dspace.unimap.edu.my/123456789/12126
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    • School of Mechatronic Engineering (Articles) [319]
    • Paulraj Murugesa Pandiyan, Assoc. Prof. Dr. [113]

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