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TABLE OF CONTENTS
TITLE PAGE
ABSTRACT
CHAPTER ONE
INTRODUCTION
1.1. BACKGROUND
1.2 SIGNIFICANCE OF STUDY
1.3 STATEMENT OF PROBLEM
1.4 PROJECT OUTLINE
CHAPTER TWO
LITERATURE REVIEW AND
THEORETICALBACKGROUND
2.1 LITERATURE REVIEW
2.1.1 INTRODUCTION
2.2 REVIEW OF PAST WORKS IN THIS
AREA
2.3. FUZZY SET THEORY AND
FORCECASTING
2.3.1 FUZZY TIME SERIES
2.3.2 FUZZY LOGIC OPERATORS
2.4 MEMBERSHIP FUNCTION
2.4.1 MEMBERSHIP FUNCTIONS IN FUZZY
LOGIC
2.4.2 MEMERSHIP FUNCTIONS FOR
FUZZIFICATION
2.4. PERFORMANCE MEASURES
CHAPTER THREE
3.1 INTRODUCTION
3.2 FORECASTING ANALYSIS
CHAPTER FOUR
4.1 INTRODUCTION
4.2 SIGNIFICANCE
OF RESULT
CHAPTER FIVE
CONCLUSION AND RECOMMENDATIONS FOR
FURTHER WORK
5.1 SUMMARY
5.2 LIMITATIONS
5.3 CONCLUSION
5.4 SUGGESTIONS FOR FURTHER WORK
REFERENCE
APPENDIX
ABSTRACT
Fuzzy Time Series (FTS) plays a great role in fuzzification
of data, which is based on certain membership functions. In this thesis, a 24
weeks load demand data from PHCN was used and fuzzified based on the Gaussian
Membership Functions, after that all fuzzified data are defuzzified to get
normal form. The results obtained using the GMF (Gaussian Membership Functions)
is compared with that of the TMF (Triangular Membership Function), from which
the comparison basis was based on, qualitative performance indicator and
statistical error. The RMSE Values obtained using the GMF and the TMF are 66.5
and 17.1 respectively, while their correlation factor R is 0.98 for TMF and
0.86 for GMF. From the analysis carried out the TMF generated the least RMSE
and hence, is more suitable in forecasting for electric load.
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND
Load forecasting is of vital importance in the electricity
industry, especially in a deregulated economy like that of Nigeria. It has many
application including energy purchasing and generation, load switching,
contract evaluation, and infrastructural development. A large variety of
mathematical models have been developed and applied in carrying out load
forecasting. In this work, the Fuzzy Time Series (FTS) approach is used for the
load forecasting.
There is a planned Government policy towards unbundling the
utility (Power Holding Company of Nigeria (PHCN)) company with the objective of
improving efficiency of electricity generation, transmission, and distribution.
This emphasizes proper and effective planning, management and operations of the
network. The operation and planning of a power utility company requires an
adequate model for electric power load forecasting.
Load forecasting plays a key
role in helping an electricity utility to make important decisions on power,
load switching, voltage control, network reconfiguration, and infrastructure
development. It is extremely important for an optimal management of generation
and distribution of electric energy to have as precise as possible the load
profile prediction.
According to Abbasovand
Mamedova (2003), time series represents a consecutive series of observations
taken over equal time intervals. The application of Fuzzy Logic and fuzzy sets
to time series analysis gave rise to Fuzzy Time Series. The method to be
applied here is the method initially used by Abbasovand mamedova (2003), in
forecasting population in.......
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