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Information Seminar Paper

Title

SHORT TERM WIND SPEED PREDICTION USING ARTIFICIAL NEURAL NETWORKS BASED ONLEVENBERG-MARQUARDT OPTIMIZATION METHOD

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Abstract

 WIND SPEED PREDICTION IS CRITICAL FOR WIND ENERGY CONVERSION SYSTEMS SINCE IT GREATLY INFLUENCES THE ISSUES SUCH AS THE SCHEDULING OF A POWER SYSTEM, AND THE DYNAMIC CONTROL OF THE WIND TURBINE.IN THIS PAPER THE SHORT TERM WIND SPEED FORECASTING IN THE REGION OF ISTANBUL, TURKEY, APPLYINGTHE TECHNIQUE OF ARTIFICIAL NEURAL NETWORK (ANN) BASED ON LEVENBERG-MARQUARDT OPTIMIZATION METHODTO THE 10 MINUTE INTERVAL TIME SERIES REPRESENTATIVE OF THE SITE ISPRESENTED AND ALSO WE PRESENT A COMPREHENSIVE COMPARISON STUDY ON THE APPLICATION OF DIFFERENT ARTIFICIAL NEURAL NETWORKS ARCHITECTURESIN WIND SPEED FORECASTING AND COMPARE TRIANGULAR REDUCTION METHOD DATA WITH HECHT-NIELSEN APPROXIMATION METHOD DATA.THE DEVELOPED MODEL FOR SHORT TERM WIND SPEED FORECASTING SHOWED A VERY GOOD ACCURACY AND AGREEMENT BETWEEN PREDICTED AND REAL DATA.THE RESULTS ARE VALIDATED AND THE EFFECTIVENESS OF THE TRIANGULAR REDUCTION METHOD ANDHECHT-NIELSEN APPROXIMATION METHOD IS DEMONSTRATED.

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