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  • Volume 2018

    An Improvement In Load Forecasting Model Using Parametric Tuned Support Vector Machine (SVM) Kernel Based Functions
    (International Journal of Engineering Works)

    Vol. 5, Issue 9, PP. 154-162, September 2018
    Keywords: Short term load forecasting (STLF), Support Vector Machine, kernel function, time series, Artificial Neural Network (ANN)

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    Short term load forecasting (STLF) has gained huge interest among researchers because of its applications in economics, reliability, unit commitment (UC), economic dispatch (ED) and hydro-thermal coordination (HTC) of power systems. The aim of this study is to find an accurate algorithm as it is very important for the prediction of accurate load forecast. Support Vector Machine Regression Model (SVM-R) using different kernels i-e linear, polynomial and gaussian has been used and each kernel function effectiveness and its performance has been examined on real time series using ISO-New England utility data. LibSVM using R language is utilized in this research to employ SVM-R Model. Artificial Neural Network (ANN) is utilized to compare and check the effectiveness of proposed model and its performance by considering least Mean Absolute Percentage Error.


    1. Engr. Hamad Ullah Khan Bangash, Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan,

    2. Dr. Amjad Ullah Khattak, Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan,


    Full Text


    Engr. Hamad Ullah Khan Bangash and Dr. Amjad Ullah Khattak, "An Improvement in Load Forecasting Model using Parametric Tuned Support Vector Machine (SVM) Kerne", International Journal of Engineering Works, Vol. 5 Issue 9 PP. 154-162 September 2018.


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