Hyperbolic Tangent Basis Function Neural Networks Training by Hybrid Evolutionary Programming for Accurate Short-Term Wind Speed Prediction

2009 ◽  
Author(s):  
C. Hervás-Martínez ◽  
P.A. Gutiérrez ◽  
J.C. Fernández ◽  
S. Salcedo-Sanz ◽  
A. Portilla-Figueras ◽  
...  
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Basis Function ◽  
Evolutionary Programming ◽  
Hyperbolic Tangent ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction

Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks

2009 ◽  
Vol 72 (4-6) ◽  
pp. 1336-1341 ◽  
Author(s):  
Sancho Salcedo-Sanz ◽  
Ángel M. Pérez-Bellido ◽  
Emilio G. Ortiz-García ◽  
Antonio Portilla-Figueras ◽  
Luis Prieto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Input Data ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Artificial Neural

Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction

2009 ◽  
Vol 34 (6) ◽  
pp. 1451-1457 ◽  
Author(s):  
Sancho Salcedo-Sanz ◽  
Ángel M. Pérez-Bellido ◽  
Emilio G. Ortiz-García ◽  
Antonio Portilla-Figueras ◽  
Luis Prieto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Mesoscale Model ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Fifth Generation ◽  
Speed Prediction ◽  
Artificial Neural

scholarly journals Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4126 ◽  
Author(s):  
Yidi Ren ◽  
Hua Li ◽  
Hsiung-Cheng Lin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wind Speed ◽  
Wind Power ◽  
Flower Pollination Algorithm ◽  
Short Term ◽  
Neural Network Models ◽  
Wind Speed Prediction ◽  
Flower Pollination ◽  
Speed Prediction

It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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