Abstract
This paper introduces the concept and practice of Neural Network architectures for wind speed prediction in wind farms. The wind speed prediction method has been analyzed by using back propagation network and radial basis function network. Artificial neural network is used to develop suitable architecture for predicting wind speed in wind farms. The key of wind speed prediction is rational selection of forecasting model and effective optimization of model performance. To verify the effectiveness of neural network architecture, simulations were conducted on real time wind data with different heights of wind mill. Due to fluctuation and nonlinearity of wind speed, accurate wind speed prediction plays a major role in the operational control of wind farms. The key advantages of Radial Basis Function Network include higher accuracy, reduction of training time and minimal error. The experimental results show that compared to existing approaches, proposed radial basis function network performs better in terms of minimization of errors.
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The authors are thankful to Suzlon Energy Ltd. for providing real time data of wind farm to carry out the research in this area.
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Communicated by G. Acampora.
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Gnana Sheela, K., Deepa, S.N. Performance analysis of modeling framework for prediction in wind farms employing artificial neural networks. Soft Comput 18, 607–615 (2014). https://doi.org/10.1007/s00500-013-1084-9
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DOI: https://doi.org/10.1007/s00500-013-1084-9