-
Time Series Analysis On Consumption Of Electricity In Kwara State
[A CASE STUDY OFFA NEPA DISTRICT OFFICE FROM 2001-2015] -
-
-
1.3 SIGNIFICANCE OF STUDY
This project is to highlight the uses of statistics and its techniques in analyzing data in respect of electricity, and forecast the future consume two pattern.
1.4 SCOPE OF THE STUDY
The study deal with consumption of electricity in Kwara State Ilorin undertaking. The data available is that of units consumed in [KWHX 10] between 1989 – 2003. The data was extracted from account and sales department of NEPA Ilorin Districts office.
1.5 AIMS AND OBJECTIVE
To identify and fit model for the study and forecast monthly value for it.
To test the variation with the year
To test for the average consumption of electricity on monthly basis.
To analyze time series of monthly power consumption in Kwara State [Ilorin undertaking].
1.6 PROBLEM AND LIMITATION OF THE STUDY
There are many limitation that hinder the extent at which research can carried out one of which is inadequacies of textbook especially relating to the topic. Another problem encountered is the negative attitude of the management of the chosen case study, feeling reluctant to supply accurate record of the authority.
1.7 DEFINITION OF TERMS
Consumer: Any person or corporate body supply with electricity by the NEPA.
Residential Domesic Consumer: A consumer who use the premises exclusive as a residence house.
Commercial consumer: A consumer who use his premises for any purpose other than exclusively as a residence or as a factory for manufacturing goods.
Industrial consumer: A consumer who use it for manufacturing goods e.g. global soap and detergent company.
1.8 ABBREVIATION USED
N.E.P.A – National Electricity Power Authority
E.C.N – Electricity Corporation of Nigeria
N.D.A – Niger Dam Authority
M.W. – Mega Watt
K.V. – Kilo-Volt
-
-
-
ABSRACT - [ Total Page(s): 1 ]ABSTRACT HERE ... Continue reading---
TABLE OF CONTENTS - [ Total Page(s): 1 ]TABLE OF CONTENTCOVER PAGE APPROVAL PAGE DEDICATION ACKNOWLEDGEMENTTABLE OF CONTENT1.1 INTRODUCTION1.2 BACKGROUND AND ITS OPERATION 1.3 SIGNIFICANCE1.4 SCOPE OF STUDY1.5 AIMS AND OBJECTIVE 1.6 PROBLEMS AND LIMITATION OF STUDY 1.7 DEFINITION OF TERMS 1.8 ABBREVIATION USEDCHAPTER TWO 2.0 LITERATURE REVIEW2.1 METHOD OF DATA ANALYSIS 2.2 TIME SERIES ANALYSIS 2.3 IMPORTANCE OF TIME SERIES 2.4 NATURE OF TIME SERIES 2.5 ... Continue reading---
BIBLIOGRAPHY - [ Total Page(s): 1 ]BIBLIOGRAPHYFreud J.E. William F.J (1970) “Modern Business Statistics†Pitman Publishing bid great Britain Murray .R, Spiegal (1961) “Theory and problems of Statistics†in S.I (Schaum’s outline series)Notational Electric Power Authority 2003 Press Clip (NEPA transformation newsletter Vol.0033) J.B BABALOLAJ.B BABALOLA “Statistics with applications (in behavioural Science, Business and Engineering) Revised Editions MR. .I.O Azeez “ ... Continue reading---
CHAPTER TWO - [ Total Page(s): 14 ]CHAPTER TWO2.0 LITERATURE REVIEW SOURCES OF DATA COLLECTION Data are piece of information collected for a certain purpose, in statistic, we can categorizes data into two types; the primary data and secondary data. 2.1 PRIMARY DATA These are data collected at sources. This is the collection of such data in direct from the object of the interest i.e. data collected as a result of research methodology e.g. result of the question and sample survey. 2.2 SECONDARY DATA ... Continue reading---
CHAPTER THREE - [ Total Page(s): 12 ]SEMI-MOVING AVERAGE METHOD This consist of separating the data parts (preferably equal) averaging the data in each part, this obtaining two points on the graph of the time points and the trend value can be determined directly with out a graph.FREE HAND METHOD This is method which consist of fitting a trend line or curve simply by looking at the graph, can be used to estimate T trend. ESTIMATION OF SEASONAL VARIATION There are different methods available for computing seasona ... Continue reading---
CHAPTER FOUR - [ Total Page(s): 10 ]Since we must eliminate seasonal variation, the graph label Fig. 11. The seasonal variation in our time series and it is used to remove the effect of seasonal from time series which is called de-seasonalizing a time series. This graph made us proceed be deseasonilize our data as table 4.4 Moving average graph and original data of the montly consumption from 1998-2003 The graph of the original data shows in the figure by the solid line graph of the moving average is shown in broken ... Continue reading---
CHAPTER FIVE - [ Total Page(s): 1 ]CHAPTER FIVE5.1 SUMMARY OF FINDINGS The success of any analysis can only but judged by extent to which to which the objectives of the research have been achieved. While the series contain the upward trend figure 1 and 2 shows it to be very week one, the same conclusion can be drawn about the seasonal factor, these shows in table 4:3. However a different conclusion can be drawn about = periodic cyclical reregulation factor. By residual reasoning, it can be inferred that ... Continue reading---
-
ABSRACT - [ Total Page(s): 1 ]ABSTRACT HERE ... Continue reading---