Very Short-Term Wind Speed Prediction of a Wind Farm Based on Artificial Neural Network

Wind speed prediction is critical for wind energy conversion system since it not only can relieve or avoid the disadvantageous impact on power system, but also can enhance the competitive ability of wind power plants against others in electricity markets. The model presented in this paper was based on artificial neural network (ANN) and the selection of the model parameters was presented in detail. The autocorrelation function (ACF) of wind speed time series was used to determine the input variables of the neural network. The simulation was carried out with the proposed ANN model.The conclusion that the optimal network structure may be different corresponding to different error evaluation was drawn through a large number of simulation experiments. And the simulaiton results showed that the ANN model is less than 10.77% in terms of root mean square error and 5.86% in terms of mean absolute error compared with the persistence model.

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Improved Wind Speed Prediction Results by Artificial Neural Network Method

Exergy for A Better Environment and Improved Sustainability 2 - Green Energy and Technology ◽

10.1007/978-3-319-62575-1_46 ◽

2018 ◽

pp. 651-665

Author(s):

Asilhan Sevinc Sirdas ◽

Akatas Nilcan ◽

Izgi Ercan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wind Speed ◽

Neural Network Method ◽

Wind Speed Prediction ◽

Network Method ◽

Artificial Neural Network Method ◽

Speed Prediction ◽

Artificial Neural

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A New Hybrid Approach for Wind Speed Prediction Using Fast Block Least Mean Square Algorithm and Artificial Neural Network

Mathematical Problems in Engineering ◽

10.1155/2016/8395751 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Ummuhan Basaran Filik

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wind Speed ◽

Hybrid Approach ◽

Mean Square ◽

Least Mean Square ◽

Wind Speed Prediction ◽

Speed Prediction ◽

Artificial Neural ◽

Prediction Approach

A new hybrid wind speed prediction approach, which uses fast block least mean square (FBLMS) algorithm and artificial neural network (ANN) method, is proposed. FBLMS is an adaptive algorithm which has reduced complexity with a very fast convergence rate. A hybrid approach is proposed which uses two powerful methods: FBLMS and ANN method. In order to show the efficiency and accuracy of the proposed approach, seven-year real hourly collected wind speed data sets belonging to Turkish State Meteorological Service of Bozcaada and Eskisehir regions are used. Two different ANN structures are used to compare with this approach. The first six-year data is handled as a train set; the remaining one-year hourly data is handled as test data. Mean absolute error (MAE) and root mean square error (RMSE) are used for performance evaluations. It is shown for various cases that the performance of the new hybrid approach gives better results than the different conventional ANN structure.

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