Abstract
The effective operation of the wind farm is decided by the accuracy of forecasted wind speed and the wind power generated. In this paper, an improved neural network based on radial basis function is implemented for very short-term duration forecasting. For training, the neural network Gaussian function is included in the hidden layer to find the initial values which are key parameters for training the neural network. A case study is carried out for the wind farm located at Seshachalam hills near Tirupati as the target location. Test data are considered for different months of 2017 for training and compared with other artificial neural network methods. The accuracy of all the methods has been studied and presented, showing that the proposed model is having less forecast error.
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We acknowledge the use of data provided by NARL through www.narl.gov.in.
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Hemanth Kumar, M.B., Saravanan, B. (2019). Wind Speed and Power Forecast for Very Short Time Duration Using Neural Network Approach—A Case Study. In: Mishra, S., Sood, Y., Tomar, A. (eds) Applications of Computing, Automation and Wireless Systems in Electrical Engineering. Lecture Notes in Electrical Engineering, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-13-6772-4_11
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DOI: https://doi.org/10.1007/978-981-13-6772-4_11
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