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گواهی نمایه سازی مقاله Short-term load forecasting of Urmia city with hybrid k-means, VSS LMS” learning method for RBF neural network

عنوان مقاله: Short-term load forecasting of Urmia city with hybrid k-means, VSS LMS” learning method for RBF neural network
شناسه (COI) مقاله: IEAC02_040
منتشر شده در کنفرانس بین المللی فناوری و مدیریت انرژی در سال ۱۳۹۴
مشخصات نویسندگان مقاله:

Mehdi Panahi - Department of technical and engineering Saveh Azad University Tehran - Iran
Ehsan Mostafapour - Dept. of electrical and computer engineering Urmia University Urmia - Iran
Reza Ghaderi - Department of electrical engineering Shahid Beheshti University Tehran - Iran
Morteza Farsadi - Dept. of electrical and computer engineering Urmia University Urmia - Iran

خلاصه مقاله:
in this paper we investigate the performance of a hybrid learning algorithm for RBF network in the application of short-term load forecasting. In this method the algorithm forfinding radial basis function centers of hidden layer is k-means and the algorithm for training the weights of output layer isadaptive variable step-size algorithm. We proved this method isboth accurate and fast in comparison with other presented schemes. Also we demonstrated that this method requires lesscomputational processing and can perform well when amount of the input data is large. Our simulation results for Urmia city – Iran, show there is up to 30 percent improvement in processing time and 37% improvement in prediction accuracy whencompared with previously improved k-means learning

کلمات کلیدی:
RBF network, load forecasting, variable step-size LMS algorithm, hybrid learning

صفحه اختصاصی مقاله و دریافت فایل کامل: https://www.civilica.com/Paper-IEAC02-IEAC02_040.html