Transcript 第九組 - neural network.pptx

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
Outline
 Introduction
 Method
 Experiments
 Conclusion
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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.
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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)
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Introduction(3/6)
 The proposed hybrid model is a combination of SOM and MLP to
understand a much better prediction system.
›
SOM is used for clustering the input data.
›
MLP is used for prediction.
 The performance of neural network model is determined by the
minimal root means squared error (RMSE).
𝑅𝑀𝑆𝐸 =
𝑁 (𝑌𝑖 −𝑌′𝑖 )
𝑖=1
𝑁
2
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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.
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Introduction(5/6)
 The winner can be found using Euclidean distance method：
winner, 𝐶 = arg min ∐𝑥 𝑡 − 𝑊𝑖 ∐ , 𝑖 ∈ 1, … , 𝑚
 The weight updating rule in SOM network：
𝑊𝑖 𝑡 + 1 = 𝑊𝑖 𝑡 + 𝜂𝑁𝑐(𝑡)(𝑥 𝑡 − 𝑊𝑖 (𝑡)) ∀ 𝑖 ∈ 𝑁𝑐
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Introduction(6/6)
 MLP is one of the perceptron learning rules based on computing model
in artificial neural network .
 This network has mainly two steps.
›
The first step is to find the output of each layer.
›
The second step is to be performed, to update the weight and minimize the errors.
𝑌 = 𝑓(
𝑚
𝑗=1 𝑍𝑗 𝑊𝑗 )
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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.
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2nd
5th MLP input Layer.
Data Normalization.
7th MLP
output
Layer.
6th MLP
hidden
Layer.
Method
(2/5)
1 input data.
st
th SOM
3rd SOM4input
data.
output data.
Fig.1 hybrid computing model.
8th Data de-Normalization.
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Method(3/5)
 The inputs are wind speed, wind direction and temperature.
 The predicted wind speed is an output of the proposed model.
S. No
Input parameters
Units
Range of the
parameters
1
Wind speed
m/s
1–12
2
Wind direction
Degree
1–347
3
Temperature
Degree. C
24–36
Table.1 Input parameters of the proposed model.
Fig. 2 Data used in the study.
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Method(4/5)
 The set up parameter includes learning rate, epoch, dimensions and so
forth.
MLP network
SOM network
Learning rate
0.25
Output Cluster
4
Output neuron
1
No. of hidden layer
0
No. of hidden layer
1
Input Neurons
3
Input neurons
3
No. of Epochs
2000
No. of Epochs
2000
Threshold
1
Table.2 Parameter selection of neural network.
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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.
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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 output Predicted output
5.916
5.7
4.8566
4.8
6.665
6.5
6.0321
5.8
5.3363
5.2
5.1284
5
5.3425
5.2
4.8657
4.4
4.5047
4.5
6.2059
6.1
Fig.3 Actual/predicted output waveform.
Table.3 Sample outputs.
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Experiments(2/2)
 The RMSE value of hybrid computing model is less than conventional
MLP, BPN and RBF neural networks.
Neural Network Models
RMSE
Conventional MLP network
0.231
Back propagation neural network
0.21
Radial basis function neural network
0.18
Proposed hybrid neural network
0.0828
Table.4 Performance of Neural Network models.
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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.
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Thanks for your attention
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