Abstract
An accurate short-term wind speed prediction algorithm based on the efficient kernel ridge pseudo inverse neural network (KRPINN) variants is proposed in this paper. The use of nonlinear kernel functions in pseudo inverse neural networks eliminates the trial and error approach of choosing the number of hidden layer neurons and their activation functions. The robustness of the proposed method has been validated in comparison with other models such as pseudo inverse radial basis function (PIRBF) and Legendre tanh activation function based neural network, i.e., PILNNT, whose input weights to the hidden layer weights are optimized using an adaptive firefly algorithm, i.e., FFA. However, since the individual kernel functions based KRPINN may not be able to produce accurate forecasts under chaotically varying wind speed conditions, a linear combination of individual kernel functions is used to build the multi kernel ridge pseudo inverse neural network (MK-RPINN) for providing improved forecasting accuracy, generalization, and stability of the wind speed prediction model. Several case studies have been presented to validate the accuracy of the short-term wind speed prediction models using the real world wind speed data from a wind farm in the Wyoming State of USA over time horizons varying from 10 minutes to 5 hours.
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S. P. Mishra received the B.Tech. and M.Tech. degrees in electrical engineering from the KIIT University, India in 2010 and 2013, respectively. Currently, he is a Ph.D. degree candidate in electrical engineering at Multidisciplinary Research Centre, Sikhsha “O” Anusandhan University, India. He is a member of IEI, SESI and ISTE.
His research interests include renewable energy, artificial intelligence, neural net and control theory.
P. K. Dash received the M.Eng. degree in electrical engineering from Indian Institute of Science, India in 1964, received the Ph.D. degree in electrical engineering from the Sambalpur University, India in 1972, and received the D. Sc. degree in electrical engineering from the Utkal University, India, in 2003. Besides, he had his post-Doctoral education from the University of Calgary, Canada in 1975 and 1976, and he spent more than three decades at the National Institute of Technology, India as a professor and head of the Department of Electrical Engineering. Currently he is working as a director (research and consultancy) at Sikhsha “O” Anusandhan University, India. He had several visiting appointments in Canada, USA, Switzerland, Malaysia and Singapore. He has published more than 600 international journal and conference papers. He is a fellow of Indian National Academy of Engineering, and senior member, IEEE.
His research interests include renewable energy, micro and smart grid, machine intelligence, signal processing and control.
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Mishra, S.P., Dash, P.K. Short term wind speed prediction using multiple kernel pseudo inverse neural network. Int. J. Autom. Comput. 15, 66–83 (2018). https://doi.org/10.1007/s11633-017-1086-7
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DOI: https://doi.org/10.1007/s11633-017-1086-7