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Time Series Analysis On Consumption Of Electricity In Kwara State
[A CASE STUDY OFFA NEPA DISTRICT OFFICE FROM 2001-2015]
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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 seasonal variation. According to “Spiegelâ€, there are three ways of estimating seasonal variation, they are
Simple average method
Percentage trend
Ratio to moving average method
SIMPLE AVERAGE METHOD
The average value for each month for all years, compute monthly indices in the following manner.
Month indices = (Average of the month)/(Total of the month) ×100
PERCENTAGE TREND METHOD
Compute monthly (or quantity) trend figures and experience the original figure as percentage of the correspondence trend figure.
RATIO TO MOVING AVERAGE
Ratio taking
X_t=T_t∙S_t∙I_t
X_t=T_t∙S_t
S_t=X_t/T_t
FORECASTING
The importance of time series analysis is to forecast value based on the past or previous occurrence. According to “Richard L. Mills†a forecast is quantitative estimate about the likelihood even based on the previous information which is in form of model of either of the following simultaneous equation, simple equation or time series model by extrapolating the model out beyond the period over which they are estimate to make forecast about the future, with this we have the following types of forecasting.
Short term forecast
Intermediate forecast
Long term forecast
SHORT TERM FORECASTING
This covers a day to one year and used primary for short time control and sales forecast, method involve includes time series, regression analysis
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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---
CHAPTER ONE - [ Total Page(s): 2 ]CHAPTER ONE1.0 INTRODUCTION The primary aim of National Electrical Power Authority (NEPA) is in cardinal point which is to generate, transmission, distribution and sales electricity within and outside country. National Electric Power Authority NEPA can be regarded as heart beat of the nation economy as the operation of machines use in industries and most household equipment depend on electricity. Today National Electric Power Authority (NEPA) meets total maximum energy demand from t ... 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 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---
