Short Term Load Forecasting Using A Hybrid Model Based On Support Vector Regression
Aliasghar Baziar, Abdollah Kavousi-Fard
Index Terms: Support Vector Regression (SVM), Krill Herd (KH) Algorithm, Short Term Load Forecasting (STLF).
Abstract: This paper proposes a new hybrid method based on support vector regression (SVR) to predict the load value of power systems accurately. The proposed method will use the SVR to overcome some deficiencies such as overfitting and complicated structure that exist in the neural network. In order to find the optimal values of the parameters, krill herd (KH) algorithm is used as the optimizer. The KH algorithm can explore the problem search space for reaching the best structure for the SVR when training. In order to check the performance and accuracy of the proposed hybrid method, the empirical load data from the Fars Regional Company are used as the test data. The simulations show the high reliable and accuracy of the proposed method.
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 Fars Electrical Power Company: http://www.frec.co.ir