PERPUSTAKAAN UTHM
*30000001883623*
KOLEJ UNIVERSITITEKNOLOGI TUN HUSSEIN ONN
PENGESAHAN STATUS LAPORAN PROJEK SARJANA
FORECASTING SUNSPOT NUMBERS USING NEURAL NETWORK: EFFECT TO THE
ELECTRICAL SYSTEM
SESIPENGAJIAN: 2006/2007
Saya REZA EZUAN BIN SAMIN mengaku membenarkan Laporan Projek Sarjana ini disimpan di Perpustakaan dengan syarat-syarat kegunaan seperti berikut:
1. 2 . 3.
Laporan Projek Sarjana adalah hakmilik Kolej Universiti Teknologi Tun Hussein Onn. Perpustakaan dibenarkan membuat salinan untuk tujuan pengajian sahaja. Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi pengajian tinggi. ** Sila tandakan (V)
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SULIT
TERHAD
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(TANDATANGAN PENULIS)
Alamat Tetap:
7 (TANDATAF JG iN PENYELIA)
63 J ALAN BESAR,
TONGKANG PECAH,
83010 BATU PAHAT,
JOHOR.
Tarikh:
PROF. MADYA SITI HAWA BT RUSLAN
Nama Penyelia
Tarikh:
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daripada pihak berkuasa/organisasi berkenaan dengan menyatakan sekali sebab dan tempoh laporan ini perlu di kelaskan sebagai SULIT atau TERHAD.
FORECASTING SUNSPOT NUMBERS USING NEURAL NETWORK: EFFECT
TO THE ELECTRICAL SYSTEM
REZA EZUAN BIN SAMIN
A project report submitted in partial fulfillment of the requirements for
the award of the degree of
Master of Electrical Engineering
Faculty of Electrical and Electronics Engineering
Kolej Universiti Teknologi Tun Hussein Onn
NOVEMBER, 2006
"I hereby declare that the work in this report in my own except for quotations and
summaries which have been duly acknowledged"
Student
REZA EZUAN BIN SAMIN
_ ; ^ g / ( ] / ^ o f c
Supervised by
Supervisor I
Supervisor II
DR. AZME BIN KHAMIS
iii
ACKNOWLEDGEMENT
Assalamualaikum. First of all I would like to thank Allah The Almighty for
giving me the strength to complete my research as one of the requirement for my Master
degree.
I would also like to thank Associate Professor Siti Hawa Bt. Ruslan, my project
supervisor for her support and encouragement all the time especially during the
difficulties that I faced during the completion of my research. Not to forget, Dr Azme
Bin Khamis, my project co-supervisor for his guidance and opinion especially in the
area of Neural Network.
I would also like to thank my parents for their support, love and understanding
during the completion of my Master study. I also like to thank my beloved fiance for the
understanding and support all the time.
Last but not least, to all my colleagues that also gave the encouragement and
opinion in making my research a great success. Wassalam.
IV
ABSTRACT
The purpose of this research is to develop the forecasting system of Sunspot
Numbers that highly related to Geomagnetic Induced Current (GIC). This geomagnetic
induced current (GIC) have the effect to the electrical system especially to the
transformers. Sunspot data obtained from the National Geophysical Data Center
(NGDC) ranging from 1700 until 2005 is analyzed using Neural Network (NN) using
the MATLAB 7.0 Graphic User Interface (GUI) method computer program called
"Sunspot Neural Forecaster" so that the analysis and simulation of the sunspot data can
be done easily and more user friendly. First, a comparison analysis between
Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN) is done to
choose the best NN type for the next analysis. The second stage of the analysis involved
the selection of NN training algorithm between Levenberg Marquardt, Resilient
Backpropagation and Gradient Descent. As in the selection of NN type analysis, the
best NN training algorithm is selected for the next analysis. The next analysis involved
the selection of NN models between NN1, NN2, NN3 and NN4 and the best models is
selected for the last analysis which is the transfer function analysis. The NN transfer
function analysis involved Tansig/Purelin and Logsig/Purelin transfer function for the
hidden layer and output layer respectively. Based from the analysis that have been done,
FNN using Levenberg Marquardt training algorithm with NN2 model and
Tansig/Purelin transfer function are used for forecasting the sunspot data. The
forecasting result obtained shows the system managed to forecast the sunspot numbers.
V
ABSTRAK
Tujuan penyelidikan ini adalah untuk membangunkan suaru sistem ramalan
"Sunspot Neural Forecaster" bagi meramal nombor bintik suria (Sunspot Numbers) yang
mempunyai kesan terhadap arus teraruh geomagnetik, {Geomagnetic Induced Current,
GIC). Fenomena GIC ini memberi impak kepada sistem elektrik terutamanya sistem
transformer. Data bagi penyelidikan ini diperolehi daripada National Geophysical Data
Center (NGDC) dari tahun 1700 hingga 2005. Analisis kemudian dijalankan
menggunakan data tersebut dengan menggunakan rangkaian neural menggunakan
perisian MATLAB 7.0 diberi nama "Sunspot Neural Forecaster" menggunakan kaedah
GUI agar analisis bagi nombor bintik suria dapat dibuat dengan lebih mudah serta
mesra pengguna. Pada peringkat awal, analisis perbandingan dibuat antara FNN dan
RNN dan rangkaian neural terbaik dipilih bagi analisis seterusnya. Analisis seterusnya
merupakan analisis bagi algoritma pembelajaran yang berbeza. Tiga jenis analisis
algoritma pembelajaran telah dibuat iaitu Levenberg Marquardt, Resilient
Backpropagtion dan Gradient Descent dan algoritma pembelajaran yang memberikan
prestasi terbaik akan dipilih bagi analisis seterusnya. Analisis seterusnya merupakan
analisis bagi model yang berbeza iaitu NN1, NN2, NN3 dan NN4 yang mana model
terbaik akan dipilih bagi analisis terakhir iaitu analisis bagi fungsi pindah yang berbeza
iaitu Tansig/Purelin dan Logsig/Purelin bagi fungsi pindah pada lapisan tersembunyi
serta lapisan keluaran. Berdasarkan analisis-analisis yang telah dibuat, suatu model
rangkaian neural menggunakan FNN dengan algoritma pembelajaran Levenberg
Marquardt menggunakan model NN2 serta Tansig/Purelin masing-masing sebagai
fungsi pindah pada lapisan tersembunyi dan lapisan keluaran digunakan bagi meramal
nombor bintik suria dengan tepat.
vi
TABLE OF CONTENTS
CHAPTER TITLE PAGE
ACKNOWLEDGEMENT iii
ABSTRACT iv
ABSTRAK v
TABLE OF CONTENTS vi
LIST OF TABLES ix
LIST OF FIGURES x
LIST OF SYMBOLS xiii
LIST OF ABBREVIATION xiv
LIST OF APPENDICES xvi
I INTRODUCTION 1
1.1. Research Background 1
1.2. Problem Statement 2
1.3. Importance of Study 3
1.4. Research Objective 3
1.5. Scope of Project 4
1.6. Thesis Outline 5
vii
II LITERATURE STUDY 7
2.1 Introduction 7
2.2 What Is Sunspot Numbers 8
2.3 Sunspot Numbers & Geomagnetic Induced Current 11
2.4 GIC and Its Effect to Electrical System 12
2.5 Sunspot Forecasting 20
2.6 The Role of Forecast 24
ffl RESEARCH METHODOLOGY 27
3.1 Introduction 27
3.2 Introduction to Neural Network 27
3.2.1 Feedforward Neural Network 31
3.2.2 Recurrent Neural Network 33
3.2.3 Training Algorithm 34
3.2.4 Transfer Function 35
3.2.5 Improving Generalization 38
3.2.6 Neural Network Application in Forecasting 39
3.3 Development of NN System 40
3.3.1 Data Collection 41
3.3.2 Preparing of Input and Output Data 41
3.3.3 Design of Neural Network Model 42
3.3.4 Network Training 44
3.4 Summary 44
IV PROGRAMMING & GRAPHIC USER INTERFACE (GUI) 45
4.1 Introduction 45
4.2 GUI for Sunspot Neural Forecaster 46
4.2.1 Simulation for MSE and Correlation Analysis 50
viii
V RESULT AND DISCUSSION 53
5.1 Introduction 53
5.2 Selection of NN Type 54
5.3 Feedforward Neural Network Analysis 5 6
5.3.1 Selection of Training Algorithm 56
5.3.2 Selection of Model 60
5.3.3 Selection of Transfer Function 73
5.3.4 Optimized NN parameters analysis 77
5.4 Sunspot numbers forecasting 79
VI CONCLUSION 82
6.1 Conclusion 82
6.2 Recommendation for Future Works 83
REFERENCES 86
APPENDIX 91
ix
LIST OF TABLES
3.1 Combination of transfer function 43
5.1 MSE performance analysis for different NN type 54
5.2 MSE Performance analysis for different training
algorithm. 57
5.3 NN1 MSE and correlation analysis 61
5.4 NN2 MSE and correlation analysis 63
5.5 NN3 MSE and correlation analysis 65
5.6 NN4 MSE and correlation analysis 67
5.7 Average MSE performance analysis for different
models 71
5.8 MSE performance analysis for different transfer
function 74
5.9 Forecast value of sunspot numbers 80
X
LIST OF FIGURES
NO OF FIGURE TITLE PAGE
1.1 Time series plot for Sunspot number from 1700 until 2005 4
2.1 Sunspots observed from the sun 9
2.2 Sunspot and geomagnetic activity 12
2.3 Six steps of sunspot chain from the Sun to the ground 13
2.4 Ejection from the sun travels to earth and distorts earth
magnetic field 13
2.5 Half cycle saturation of power transformers due to GIC 15
2.6 Relationship between sunspot numbers and major
transformer breakdown due to GIC 16
2.7 Observed Regional GIC Index (RGI) as measured at
the Ottawa observatory on 12-14 March 1989 18
2.8 Disturbance environments observed by region on
13 March 1989 19
2.9 The Salem nuclear plant transformer damage due to GIC
half cycle saturation of transformer on 13-14 March 1989 20
3.1 Main components of neurons 28
3.2 Neural network main components 29
3.3 Feedforward Neural Network (FNN) 32
3.4 Recurrent Neural Network (RNN) 33
3.5 Linear transfer function 36
3.6 Log Sigmoid transfer function 37
xi
3.7 Tangent Sigmoid transfer function 37
3.8 NN system development flow 40
4.1 GUI for "Sunspot Neural Forecaster" 46
4.2 Actual vs NN prediction 47
4.3 Multiple hidden nodes analysis at MATLAB command
window 49
4.4 GUI simulation for MSE and correlation analysis 50
4.5 Flow chart manual for "Sunspot Neural Forecaster" 52
5.1 FNN MSE performance analysis 55
5.2 RNN MSE performance analysis 55
5.3 MSE performance for Resilient Backpropagation algorithm 58
5.4 MSE performance for Gradient Descent 58
5.5 MSE performance for Levenberg Marquardt 59
5.6 NN1 MSE performance analysis 62
5.7 NN1 correlation analysis 62
5.8 NN2 MSE performance analysis 64
5.9 NN2 correlation analysis 64
5.10 NN3 MSE performance analysis 66
5.11 NN3 correlation analysis 66
5.12 NN4 MSE performance analysis 68
5.13 NN4 correlation analysis 68
5.14 MSE training performance for different models 69
5.15 MSE validation performance for different models 70
5.16 MSE testing performance for different models 70
5.17 Average MSE performance for different models 72
5.18 NN2 Tansig/Purelin MSE performance analysis 75
5.19 NN2 Logsig/Purelin MSE performance analysis 75
5.20 NN2 average MSE performance analysis for different
transfer function 76
5.21 Actual vs. NN prediction for optimized NN parameters 78
5.22 Current & forecast value of sunspot number (1700-2025) 79
Forecast value of sunspot numbers
De-rectification of the sunspot numbers
LIST OF SYMBOLS
Transfer function
Connection matrix from input layer to hidden layer
Bias vector
Connection matrix from hidden layer to output layer
Function
Nonlinear mapping
Function argument
Input
Time lag
Number of observation
Actual input
Actual inputs at their maximum
Actual inputs at their minimum
Scaled input
Actual observation
Output of model
Output vector
Observation with m input
Observation with m+1 input
Observation with N-m patterns
Observation at time t
LIST OF ABBREVIATIONS
ARV Average Relative Variance
CME Coronal Mass Ejection
CT Current Transformer
DSF Disappearing Filaments
EHV Extra High Voltage
EPvNN Elman Recurrent Neural Network
FFN Feedforward Neural Network
GA Genetic Algorithm
GEA Genetic and Evolutionary Algorithm
GIC Geomagnetic Induced Current
GMDH Group Method of Data Handling
GRNN General Regression Neural Network
GUI Graphic User Interface
MAE Mean Absolute Error
MAPE Mean Absolute Percentage Error
MLP Multi Layer Perceptron
MSE Mean Square Error
NGDC National Geophysical Data Center.
NN Neural Network
RGI Regional GIC Index
RNN . Recurrent Neural Network
SETAR Self Exciting Threshold Autoregressive
SSN Sunspot Numbers
Self Organizing Map
Time delay Added Evolutionary Forecasting
LIST OF APPENDICES
APPENDIX TITLE
A. 1 Sunspot Numbers Data (1700-2005)
CHAPTER I
INTRODUCTION
1.1. Research Background
Solar activity forecasting is an important topic for various scientific and
technological areas like space activities related to operation of low earth orbiting
satellites, electric power transmission line, high frequency radio communications and
geophysical applications. The particles and electromagnetic radiations flowing from
solar activity outbursts are also important for long term climate variations and thus it is
very important to know in advance the phase and amplitude of the next solar and
geomagnetic cycles.
Nevertheless, the solar cycle or sunspot numbers is very difficult to predict on
the basis of time series of various proposed indicators, due to high frequency content,
noise contamination, high dispersion level and high variability both in phase and
amplitude, with intermittent behavior at different scales. This topic is also complicated
by the lack of a quantitative theoretical model of the Sun's magnetic cycle. Many
attempts to predict the future behavior of the solar or sunspot activity are well
2
documented in the literature. Numerous techniques for forecasting are developed to
accurately predict phase and amplitude of future solar cycles, but with limited success.
Depending on the nature of the prediction methods, five classes can be distinguish: 1)
Curve fitting; 2) Precursor; 3) Spectral; 4) Neural Networks; 5) Climatology.
Several method of forecasting the sunspot numbers have been developed by M.
Salvatore and C. Francesco (2006), Dmitriev A.V et.al (1999), Fessant, F, Bengio, S and
Collobert, D. (2000) and L. Ming (1990). All of the researchers have used Neural
Network (NN) in the forecasting system. In term of the NN method, there are many NN
type such as Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN)
that have been used by the researchers and each one have their own reason in selecting
the NN type that they have chosen.
1.2. Problem Statement
A model is to be extracted from the Sunspot Number (SSN) ranging back from
1700 until 2005. This model will be used to forecast the next sunspot number from year
2006 until 2025. In order to forecast the model, Neural Network (NN) system will be
used using the MATLAB 7.0 software.
3
1.3. Importance of Study
It is hope that by forecasting the Sunspot Number, it will help as a preventive
action in protecting our electrical system due to the effect of Geomagnetic Induced
Current (GIC). This is due to that sunspot numbers is highly related with the GIC
phenomena.
1.4. Research Objective
The objectives of this research are as follows:
1. To develop a prototype forecast system for predicting the solar activity using
MATLAB software. Instead of using the ordinary and less user friendly command
window in MATLAB, more user friendly graphic user interface (GUI) is used. By
forecasting the related data, it is hope that it will help in preventing the Geomagnetic
Induced Current (GIC) from affecting the electrical system.
2. To determine the effect of NN parameters such as number of hidden nodes, transfer
function and learning algorithm to the performance of the system.
3. To determine the optimum NN parameters in order to forecast the sunspot numbers.
4
1.5. Scope of Project
This project presents the NN applications for the development of expert system
for forecasting the solar activity based on the sunspot data that strongly affect the earth
communication operation. For the analysis and development of the system, MATLAB
7.0 will be used.
The sunspot data ranging from 1700 until 2005 that was used in this research was
obtained from the National Geophysical Data Center (NGDC) through the ftp server:
ftp://ftp.ngdc.noaa.gov/STP/SOLAR DATA/SUNSPOT NUMBERS/. Figure 1.1
indicates the time series plot for Sunspot numbers from 1700 until 2005. The complete
sunspot data can be seen in the Appendix.
200 180
Year
Figure 1.1: Time series plot for Sunspot number from 1700 until 2005.
5
1.6 Thesis Outline
The next chapter will focus on the literature study and brief explanation about the
effect of sunspot numbers to the electrical system. In term of the literature study, it will
not only discuss about the NN method in forecasting the sunspot numbers but also other
methods such as time series and genetic algorithm.
Chapter 3 will discuss on the research methodology which were used in this
research. Brief explanation about NN and the NN parameters that will be used in the
analysis will be made. Furthermore, the procedure of the NN development in this
research will also be discussed.
Chapter 4 will discuss about the programming and the graphic user interface
(GUI) that have been developed using MATLAB 7.0 software. In this chapter also, brief
explanation about how to use the "Sunspot Neural Forecaster" interface will also be
highlighted.
Results and discussion about all the NN analysis are in chapter 5. In this chapter,
the analysis begins with comparison analysis between Feedforward Neural Network
(FNN) and Recurrent Neural Network (RNN). Then the analysis proceeds to the
training algorithm analysis where different training algorithm will be compared in order
to get the best training algorithm. The next stage of the analysis involved analysis for
different models. The models are NN1, NN2, NN3 and NN4. As in the previous
analysis, only the best models will be selected for the next analysis. Finally, the analysis
for different transfer function was done. The combination transfer functions that
involved were Tansig/Purelin and Logsig/Purelin for hidden layer and output layer
respectively. After all the analyses have been done, the optimized NN parameters were