Arzam  Muzaffar  Kotriwala  UNMKL_009994  

Wind  Speed  Prediction  Using  Radial  Basis  Function  Neural  

Network  

H53PJ3  Final  Year  Individual  Project  

Agenda

1.  Motivation

2.  Objectives & Deliverables

3.  Project Fundamentals

4.  Methodology

5.  Results

6.  Conclusion

Agenda

1.  Motivation

2.  Objectives & Deliverables

3.  Project Fundamentals

4.  Methodology

5.  Results

6.  Conclusion

Motivation | Why Predict Wind? ²  Increase in demand for renewable energy

•  Increase in crude oil prices

•  Worldwide awareness of environmental issues & energy scarcity

² Wind power characteristics

•  Environment-friendly

•  High efficiency

²  Power production capacity varies greatly with varying weather conditions

²  Short term predictions are useful for:

•  Administering wind power

•  Scheduling maintenance

•  Boosting power generation efficiency

²  To prepare for anticipated destruction caused by high speed winds and

catastrophes such as hurricanes

Motivation | Why Neural Networks?

² Wind exhibits non-linear behavior.

² Neural networks are capable of handling non-linear data.

²  A simple approach for solving various problems that are otherwise difficult

to be modeled by conventional methods

² Neural network have the ability to:

•  Learn from data/examples

•  Recognize conspicuous and hidden patterns in chronological

observations

•  Use these relationships to predict forthcoming data

²  Suitable for wind speed prediction owing to:

•  Simplicity

•  Robustness

Motivation | Applications of Neural Networks? ²  Pattern Recognition

² Optimization

²  Power Systems

² Medicine

²  Robotics

²  Control Systems

² Manufacturing

²  Signal Processing

²  Psychology

²  Forecasting

•  Weather and market trends

•  Predicting mineral exploration sites

•  Electrical & Thermal load predictions

Agenda

1.  Motivation

2.  Objectives & Deliverables

3.  Project Fundamentals

4.  Methodology

5.  Results

6.  Conclusion

Objectives Deliverables

Obtain and organize historical wind data to train network

Variables relevant to wind speed prediction identified and separated into distinct training and prediction sets

Design a RBF neural network with appropriate input parameters and architectures

Short-term wind speed forecasting models with various network configurations

Develop code to implement and train the neural network models

Validation of forecasting accuracy of RBF model with plots and error calculations

Test the performance to investigate and analyze the RBF prediction technique

Justifications for using the proposed RBF neural network design

Project Objectives & Deliverables

Agenda

1.  Motivation

2.  Objectives & Deliverables

3.  Project Fundamentals

4.  Methodology

5.  Results

6.  Conclusion

Biological Neural Networks

Learning in biological structures entails modifications to the synaptic

weight connections that link the neurons.

Artificial Neural Networks What are they?

²  Inspired by the biological neural system

²  A subset of the domain of AI

How do they work?

²  Each single neuron is connected to other neurons of a previous layer

through adaptable synaptic weights

²  Patterns are stored as a set of connection weights

Radial Basis Function Neural Network

²  Activation function = Radial Basis Gaussian function

²  RBF has been previously applied across a spectrum of engineering

problems.

²  Studies have revealed that the BP network converges at a slow pace.

²  RBF supersedes BP in terms of learning speed and approximation accuracy

and is free from the local minima problems of BP models.

Agenda

1.  Motivation

2.  Objectives & Deliverables

3.  Project Fundamentals

4.  Methodology

5.  Results

6.  Conclusion

Methodology

The RBF wind speed forecasting system is summarized as below:

Historical Data Collection

Data Assimilation

Prediction Using RBF

Comparison with Other Techniques

Methodology | Historical Data Collection ²  The data is obtained from the Weather Analytics database and is used to

train, test and validate the network.

²  The data comprises wind speed values recorded hourly in knots, measured

at the Weather Analytics meteorological station in Madrid, Spain.

²  The wind power time series data was recorded for one complete year, from

January 1, 2012 to December 31, 2012.

²  The choice of training data plays a significant role in the overall performance

and training convergence of the neural network models.

² Division of data:

•  Training data = 250 days

•  Testing data = 106 days

Methodology | Data Assimilation

² Dependent variable: Wind Speed

²  Independent variables:

Methodology | Data Assimilation

²  Linear and multiple regression used to isolate the most important

independent variables.

² Number of inputs to neural network:

•  Autocorrelation

•  Partial Autocorrelation

² Normalization of data

Methodology | Prediction

²  The neural network model uses real world historical hourly wind data as

examples to learn from.

²  Upon the presentation of each training example, the network produces an

output based on the input pattern, which is then compared with the correct

desired output of the training pattern. In the case of there existing a

difference in these two values, the synaptic weights are changed in a

direction such that that the error is reduced.

² Once trained, the RBF model is expected to perform projections and

generalizations at high speed.

² Design parameters

•  Number of hidden neurons

•  Activation function

•  Spread factor

Methodology | Model A: Wind Speed Only

Methodology | Model B: Wind Speed & Wind Direction

Methodology | Model C: Wind Speed & Surface Air Temperature

Methodology | Model D: Wind Speed, Wind Direction & Surface Air Temperature

Methodology | Model E: The Beaufort Scale

Agenda

1.  Motivation

2.  Objectives & Deliverables

3.  Project Fundamentals

4.  Methodology

5.  Results

6.  Conclusion

Results | Performance

Error Metrics:

²  Root mean square error (RMSE)

² Mean absolute error (MAE)

² Mean absolute percentage error (MAPE)

The performance of the RBF neural network model is compared with:

²  The Persistence theorem

²  Back Propagation (BP) MLP neural network

Results | Summary of RBF Models

Model # of Inputs Spread Factor Hidden Neurons

RMS Error

A 4 10 30 1.69

B 8 50 30 1.70

C 8 14 80 1.64

D 12 90 43 1.65

E 4 10 8 0.56

Results | Summary of RBF Models

Comparison of the expected one-hour ahead output with the actual output of the

neural network of the response of Model C.

Results | Comparison with Persistence

Both the neural networks significantly outperformed the persistence technique.

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RM

S Er

ror

Look Ahead Hours

RBF (VS) Persistence

RBF Persistence

Results | Comparison with MLP

Different initial connection weights of the MLP result in different training and

prediction performances.

Though the accuracies of the two networks differed slightly, the RBF model

proved to be more reliable and suitable for the task at hand.

Agenda

1.  Motivation

2.  Objectives & Deliverables

3.  Project Fundamentals

4.  Methodology

5.  Results

6.  Conclusion

Conclusion

²  The results confirm the outcomes achieved by other researchers - the

applicability of neural networks to wind speed prediction is affirmed.

²  Artificial neural networks are a reliable method for prediction.

²  It was discovered that, different network configurations directly influenced

the forecast accuracy.

²  It should be noted that the optimal choice of neural network or error metric

for a specific site may not necessarily be the most suitable option for

another site.

Conclusion

²  Both, RBF and MLP neural networks predicted the time series fairly well.

However, certain consistent trends were seen in the errors. This could mean

that the neural networks are unable to predict the series to a high degree of

precision.

²  The Radial Basis Function (RBF) network is advantageous over the Back-

propagation (BP) network in terms of consistency and reliability. Given a set

of training inputs and corresponding targets, the RBF network produced the

same result each time.

²  Predicting the Beaufort Force of the wind revealed the usefulness of using

neural networks in wind speed prediction.

Recommendations for Future Work

² Weather is a continuous, multi-dimensional, data-intensive, dynamic and

chaotic process.

² Owing to these characteristics, highly accurate weather forecasting remains

a big challenge.

²  Improvements to proposed models

•  Training the network with data of more number of years.

•  Reducing complexity of the designs – reducing number of hidden

neurons.

² Development of a single universal performance score – combining metrics

such as RMSE, MAE, MSE.