NEURAL NETWORK FORECASTING MODEL OF ENERGY CONSUMPTION
By
JOHANA BTE. MOHO. PARIS
FINAL REPORT
Submitted to the Electrical & Electronic Engineering Programme in Partial Fulfilment of the Requirements
for the Degree
Bachelor of Engineering (Hons) (Electrical & Electronic Engineering)
Universiti Teknologi PETRONAS Bandar Seri Iskandar
31750 Tronoh Perak Darul Ridzuan
© Copyright 20 11 by
Johana Bte. Mohd. Paris, 2011
CERTIFICATION OF APPROVAL
Neural Network Forecasting Model of Energy Consumption
Approved by,
by
Johana Bte. Mohd. Paris
A project dissertation submitted to the Electrical & Electronic Engineering Programme
Universiti Teknologi PETRONAS in partial fulfilment of the requirement for the
Bachelor of Engineering (Hons) (Electrical & Electronic Engineering)
DR ROSDIAZLIIBRAHIM Project Supervisor
UNIVERSITI TEKNOLOGI PETRONAS TRONOH, PERAK
SEPTEMBER 2011
CERTIFICATION OF ORIGINALITY
This is to certifY that I am responsible for the work submitted in this project, that the original work is my own except as specified in the references and acknowledgements, and that the original work contained herein have not been undertaken or done by unspecified sources or persons.
JOHANA BTE MOHO PARIS
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ABSTRACT
Natural gas is transported to consumers via pipelines. The outgoing gas flow along the pipelines is managed and monitored by a metering system. The metering system must be ensured reliable and dependable at all cost to maintain the billing integrity between distributors and customers. An existing system in Nur Metering Station, PETRONAS Gas Berhad (PGB), Kulim is held responsible to calculate the energy consumption from the sales gas produced. The system consists of a turbine meter, measuring equipments which are pressure transmitter and temperature transmitter, gas chromatography and flow computer. However, the system is a standalone system that does not have any reference system to verify its integrity.
Customers are billed according to the amount of energy consumption calculated and any error in calculation will cause loss of profit to the company and affect PETRONAS's business credibility. Therefore a neural network forecasting model of energy consumption is developed as a verification system. The model will forecast the energy consumption of outgoing gas flow and compare it with the results of the existing metering system to ensure the reliability and accuracy of the system. A few models are developed and the best model is chosen based on the performance indicator. As a result, the billing integrity between PETRONAS and the customers could be maintained and in the future if the project is expanded, it will have the potential of saving of millions of dollars to Malaysian oil and gas companies.
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ACKNOWLEDGEMENT
I am grateful to Allah S.W.T. for His blessings that I am able to complete my Final Year Project with full strength and great health. It is my pleasures to thank all individuals that have directly and indirectly made this project a success.
First and foremost, my utmost gratitude goes to my supervisor, Dr. Rosdiazli Ibrahim for his continuous guidance and support throughout this project. I am heartily thankful to him for his words of motivation and encouragement from the initial to the final level of this project.
I would also like express my greatest appreciation to Ms. Nurul Hamiza Zaharul Hisham and Ms. Maryam Jamela Ismail for their willingness to share their knowledge in completing this project. They have been really helpful in giving advices and guidance for the understanding of this project.
I gratefully acknowledge Universiti Teknologi PETRONAS (UTP), especially the Electrical and Electronic Engineering Department for giving us students the opportunity to experience doing research works and projects. It is indeed a very big chance for us to develop our technical skills and knowledge. Besides that, many thanks to Nur Metering Station, Petronas Gas Berhad (PGB) for providing data source for this project.
Last but not least, I would like to thank my family; especially my parents and my friends for their never ending support. The love and support from them has been my biggest motivation.
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TABLE OF CONTENTS
CERTIFICATION OF APPROVAL CERTIFICATION OF ORIGINALITY ABSTRACT.
ACKNOWLEDGEMENT LIST OF FIGURES • LIST OF TABLES •
CHAPTER I: INTRODUCTION
.
1.1 Chapter Overview 1.2 Background Study 1.3 Problem Statement 1.4 Project Objectives 1.5 Scope of Study
1.6 Relevancy of Project • 1.7 Feasibility of Project •
CHAPTER2: LITERATURE REVIEW
2.1 Chapter Overview 2.2 Natural Gas
.
2.3 Transportation of Gas •
2.4 Volun~e and Energy Calculation
.
2.5 Forecasting Models ofNatural Gas Consun~ption
2.6 Artificial Neural Network: A New Solution • 2.7 Network Architecture •
2.8 Learning Algorithm 2.9 Activation Function
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IV
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1 1 1 3 4 4 5 5
6 6 6 7 8
9 11 12 14 16
CHAPTER3: MEmODOLOGY . 18
3.1 Chapter Overview 18
3.2 Research Methodology 18
3.2.1 Preliminary Research Work 18
3.2.2 Model Development 19
3.2.3 Testing and Validation Work 19 3.2.4 Results Analysis and Discussion 19
3.2.5 Final Documentation 19
3.3 Project Activities 21
3.3.1 Collection and Filtration of Data 22 3.3.2 Research and Selection ofNetwork 22
Architecture
3.3.3 Determination of Learning Algorithm and 23 Activation Function
3.3.4 Determination of Training and Validation 24 Data Division
3.3.5 Determination of Number ofNeurons 25
3.4 Tools Required 25
3.5 Gantt Chart 26
3.5.1 Gantt chart: First Semester 27 3.5.2 Gantt chart: Second Semester 28
3.6 Key Milestone 29
CHAPTER4: RESULT AND DISCUSSION 30
4.1 Chapter Overview 30
4.2 Parameters Selection • 30
4.2.1 Learning Algorithm and Activation 30 Function
4.2.2 Training and Validation Data • 33 Division
4.2.3 Number of Neurons 36
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CHAPTERS:
REFERENCES APPENDICES
4.3 Neural Network Energy Forecasting Model 39
CONCLUSION AND RECOMMENDATION 5.1 Chapter Overview
5.2 Conclusion 5.3 Recommendation
APPENDIX A APPENDIXB APPENDIXC APPENDIXD APPENDIXE APPENDIXF APPENDIXG APPENDIXH
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41 41 41 42
43 49 50 51 52 53 54 55 56 60
