1. Neural network based hybrid computing model for wind speed prediction K. Gnana Sheela, S.N. Deepa Neurocomputing Volume 122, 25 December 2013, Pages 425–429 Reporter : Feng Chun-Bi, Lin Hua-Wei

2. 2 Outline Introduction Method Experiments Conclusion

3. 3 Introduction (1/6) The Renewable Energy is pollution free and abundant. The nonlinear and fluctuation nature of wind proves to be great challenge for reliability and accuracy of power system that incorporates wind speed. To obtain proper and efficient wind power utilization, the wind speed prediction plays an important factor in forecasting.

4. 4 Introduction (2/6) The main features of Neural Network are adaptability, nonlinearity, capability to learn large data and generalization ability. There are different learning methods available in neural networks, both supervised and unsupervised. ›Supervised ： Multilayer Perceptron (MLP) 、 Back Propagation Network (BPN) ›Unsupervised ： Self-Organizing Map ( SOM ) 、 Adaptive Resonance Theory (ART)

5. 5 Introduction (3/6)

6. 6 Introduction (4/6) SOM is one of the best, unsupervised, learning neural networks. The goal of SOM is to maximize the degree of similarity of pattern, minimize the similarity of pattern belonging to different clusters. The input vector finds out the winner based on the greatest similarity or smallest distance, and output unit is a cluster.

7. 7 Introduction (5/6)

8. 8 Introduction (6/6)

9. 9 Method (1/5) SOM is used for clustering of input data for determining the relationship between input vectors and MLP for prediction of data. Hybrid computing model not only can effectively reduces the error component but also predict wind speed with better accuracy and minimal errors.

10. 10 Method (2/5) Fig.1 hybrid computing model. 1 st input data. 2 nd Data Normalization. 3 rd SOM input data. 4 th SOM output data. 5 th MLP input Layer. 6 th MLP hidden Layer. 7 th MLP output Layer. 8 th Data de-Normalization.

11. 11 Method (3/5) The inputs are wind speed, wind direction and temperature. The predicted wind speed is an output of the proposed model. S. NoInput parametersUnits Range of the parameters 1Wind speedm/s1–12 2Wind directionDegree1–347 3TemperatureDegree. C24–36 Table.1 Input parameters of the proposed model. Fig. 2 Data used in the study.

12. 12 Method (4/5) The set up parameter includes learning rate, epoch, dimensions and so forth. MLP networkSOM network Learning rate0.25Output Cluster4 Output neuron1No. of hidden layer0 1Input Neurons3 Input neurons3No. of Epochs2000 No. of Epochs2000 Threshold1 Table.2 Parameter selection of neural network.

13. 13 Method (5/5) Training SOM network ›SOM learns to classify input vector according to how they are grouped in the input data. Training/testing MLP network ›Each MLP can be trained from past input data. SOM and MLP are computed after data de-normalization, and it becomes the output of the predicted wind speed.

14. 14 Experiments (1/2) The salient feature of the proposed model is the improved accuracy with minimal error. SOM divides the input data into four clusters, in order to improve the accuracy of forecasting. Actual outputPredicted output 5.9165.7 4.85664.8 6.6656.5 6.03215.8 5.33635.2 5.12845 5.34255.2 4.86574.4 4.50474.5 6.20596.1 Table.3 Sample outputs. Fig.3 Actual/predicted output waveform.

15. 15 Experiments (2/2) The RMSE value of hybrid computing model is less than conventional MLP, BPN and RBF neural networks. Neural Network ModelsRMSE Conventional MLP network0.231 Back propagation neural network0.21 Radial basis function neural network0.18 Proposed hybrid neural network0.0828 Table.4 Performance of Neural Network models.

16. 16 Conclusions The neural network based hybrid computing model is able to predict wind speed. The proposed hybrid model is definitely of a higher standard when compared with conventional MLP, BPN and RBFN models. This neural network based proposed hybrid model is very much useful for predicting wind speed in renewable energy systems.

17. Thanks for your attention 17

18. 18 Method (4/6) Data normalization is carried out to improve the accuracy of subsequent numerical computation and to obtain an infinitely better output of the model. The minimum of normalized input is '0' and maximum of normalized input is '1'.