Wind speed prediction using statistical regression and neural network

Kulkarni, Makarand A.; Patil, Sunil; Rama, G. V.; Sen, P. N.

doi:10.1007/s12040-008-0045-7

Cited by 60 publications

(28 citation statements)

References 6 publications

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“…Kulkarni et al [43] used four different statistical techniques, curve fitting, ARIMA, extrapolation with periodic function and ANN, to predict wind speed. In this study, wind speeds of previous hours were used as input.…”

Section: Indexesmentioning

confidence: 99%

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RETRACTED: Artificial neural networks applications in wind energy systems: a review

Ata

2015

Renewable and Sustainable Energy Reviews

201

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Section: Indexesmentioning

confidence: 99%

“…vii. MLFFN with statistical data weighting pre-processing reduces the number of training data [43]. This concept may be developed for WECS applications.…”

Section: Further Research Needsmentioning

confidence: 99%

RETRACTED: Artificial neural networks applications in wind energy systems: a review

Ata

2015

Renewable and Sustainable Energy Reviews

201

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“…There are a number of traditional methods for wind speed prediction; most of them involve the statistical analysis of wind speed data from the past and the development of an empirical model using mathematical methods, such as least squares curve fitting or non-linear regression ( [11]). These methods are simple but generally result in low accuracy.…”

Section: Introductionmentioning

confidence: 99%

Short term wind speed prediction using artificial neural networks

Lodge

2014

2014 4th IEEE International Conference on Information Science and Technology

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As an alternative to fossil fuels, wind is a plentiful, clean, and renewable natural resource for energy. Essentially, power generation from wind depends on wind speed; thus, wind speed prediction becomes increasingly important for modern wind farm management and supply-demand balancing in the Smart Grid. However, wind speed is generally very difficult to estimate, due to its non-stationary and intermittent nature.In this paper, an approach based on artificial neural network (ANN) is developed. The neural network is trained and tested using data from the National Wind Technology Center. Computer simulation results show that the proposed neural network model can successfully predict wind speed in real-time.Index Terms -Wind speed prediction, artificial neural networks.

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“…The physical method uses simple and higher order equations and involves physical quantities of the real time system. The statistical approaches carry out the relation between the existing and forecasted output whose parameters are estimated with the available data [6]. The statistical methods work on both linear and nonlinear models.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

Ranganayaki

Deepa

2016

The Scientific World Journal

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Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

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Wind speed prediction using statistical regression and neural network

Cited by 60 publications

References 6 publications

RETRACTED: Artificial neural networks applications in wind energy systems: a review

RETRACTED: Artificial neural networks applications in wind energy systems: a review

Short term wind speed prediction using artificial neural networks

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

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