Wind Speed Prediction Using OLS Algorithm based on RBF Neural Network

Chen, Bei; Zhao, Liang; Wang, Xin; Lu, Jiangang; Liu, Guo Yao; Cao, Rui; Liu, Jin Bo

doi:10.1109/appeec.2009.4918972

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…Neural network approach; Support vector machine; Fuzzy and clustering approach [6,[15][16][17][18][19] Having a high ability of fault tolerance do not require accurate mathematical models with each man-machine interaction; Obtaining a satisfactory performance in non-linear time series forecasting [18] Easily getting into local optimum, over-fitting and exhibiting the relatively low convergence rate; Having a relatively low accuracy and lack for systematization [19].…”

Section: Artificial Intelligence Modelsmentioning

confidence: 99%

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Yao

Wang

2018

Sustainability

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Abstract:In the last few years, researchers have paid increasing attention to improving the accuracy of wind speed forecasting because of its vital impact on power dispatching and grid security. However, it is difficult to achieve a good forecasting performance due to the randomness and intermittency characteristics of wind speed time series. Current forecasting models based on neural network theory could adapt to various types of time series data; however, these models ignore the importance of data pre-processing and model parameter optimization, which leads to poor forecasting accuracy. In this paper, a new hybrid model is developed for short-term multi-step wind speed forecasting, which includes four modules: (1) the data pre-processing module; (2) the optimization module; (3) the hybrid nonlinear forecasting module and (4) the evaluation module. In order to estimate the forecasting ability of the proposed hybrid model, 10 min wind speed data were applied in this paper as a case study. The experimental results in six real forecasting cases indicate that the proposed hybrid model can provide not only accurate but also stable performance in terms of multi-step wind speed forecasting can be considered an effective tool in planning and dispatching for smart grids.

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Section: Artificial Intelligence Modelsmentioning

confidence: 99%

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Yao

Wang

2018

Sustainability

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“…After that, backpropagation learning step is applied and training of network starts. 16 The parameter values in ANNs affect the results significantly. Selection of the number of nodes forming the network structure and the activation function that enables the relationship between the nodes and output function is important.…”

Section: B Rbf Networkmentioning

confidence: 99%

Short-Term Wind Speed Prediction Based on Artificial Neural Network Models

Kırbaş

Kerem

2016

Measurement and Control

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TurkeyWind energy has an important place in renewable energy sources. Biggest challenges in wind energy production are the variability of the wind and difficulty of estimation of true wind speed. In this study, 25,777 records have been taken from wind measurements carried out at Mehmet Akif Ersoy University campus. Records include meteorological data such as wind speed at different heights/altitudes, wind direction, temperature, pressure and humidity. In order to estimate the wind speed that may occur at 61 m altitude, multilayer perceptron and radial basis function methods have been used. During the application phase, 100 artificial neural networks were trained and performance evaluations of these networks were done. The obtained results show that the wind speed at 61 m can be estimated with 99% accuracy using artificial neural network when other meteorological data are taken as input.

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“…Wind speed prediction models using Taboo Search (TS) algorithm, recurrent fuzzy neural network, and adaptive neurofuzzy inference system have also been developed to enhance the accuracy of the estimated wind speed and to reduce the computation time [18–20]. Chen et al [21] modeled a prediction system employing OLS (Orthogonal Least Squares) algorithm which measures the hidden nodes based on RBFN. This model predicted average hourly wind speed in one hour ahead.…”

Section: Related Workmentioning

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 OLS Algorithm based on RBF Neural Network

Cited by 12 publications

References 13 publications

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Short-Term Wind Speed Prediction Based on Artificial Neural Network Models

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

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