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ABSTRACT

 

Energy efficiency is paramount in the quest to achieve sustainable development in the 21st century.
Statistics in recent research have shown that in many sectors in any nation’s economy, which include
buildings, industries and transportation, energy consumption in buildings accounts for about 77%, a higher
percentage than other sectors in Nigeria; the same is true worldwide. Energy consumption forecasting is a
critical and necessary input to planning and monitoring energy usage, with particular reference to CO2 and
other greenhouse gas emissions. According to literature, very little research has been carried out in
designing models for energy consumption in institutional buildings. In this research, the African University
of Science and Technology (AUST) is considered as a case study, whereby the data collected is the monthly
energy consumption for the period 2012–2014 and 2015–2017. The data was collected from the monthly
electricity utility bills when the school was using a flat rate and when they were using a measured meter
rating respectively. The two models were designed for the monthly prediction of electricity consumption
of the buildings within the university using an artificial neural network. Results obtained from the two
models were compared and showed that the model designed using the latter dataset could be adopted to
forecast the electricity consumption of the school with respect to its population. This will further assist the
university in monitoring the trends of energy consumption, classify factors and components that impact
energy consumption within the university community and hence building policies on its usage and
consumption. Moreover the possibility of using renewable energy in the university could also be integrated
as a future work.

 

TABLE OF CONTENTS

CERTIFICATION …………………………………………………………………………………………………………………. ii
ABSTRACT …………………………………………………………………………………………………………………………. v
DEDICATION ……………………………………………………………………………………………………………………… vi
ACKNOWLEDGEMENT ……………………………………………………………………………………………………… vii
LIST OF FIGURES ………………………………………………………………………………………………………………. xi
LIST OF TABLES ……………………………………………………………………………………………………………….. xii
LIST OF ABBREVIATIONS ………………………………………………………………………………………………… xiii
CHAPTER ONE INTRODUCTION ………………………………………………………………………………………… 1
1.1 Background of Study ……………………………………………………………………………………………………. 1
1.2 Statement of the Problem ………………………………………………………………………………………………. 3
1.3 Aim and Objectives ……………………………………………………………………………………………………… 3
1.4 Expected Contributions …………………………………………………………………………………………………. 3
1.5 Scope of the Work ……………………………………………………………………………………………………….. 4
1.6 Thesis Structure …………………………………………………………………………………………………………… 4
CHAPTER TWO LITERATURE REVIEW ………………………………………………………………………………. 5
2.1 Introduction ………………………………………………………………………………………………………………… 5
2.2 Overview of Electricity Consumption in Nigeria ……………………………………………………………….. 5
2.3 Electricity Consumption in Institutional Buildings …………………………………………………………….. 8
2.4 Machine Learning ………………………………………………………………………………………………………… 9
2.4.1 Supervised Learning ………………………………………………………………………………………………. 10
2.4.2 Unsupervised Learning …………………………………………………………………………………………… 10
2.4.3 Reinforcement Learning ………………………………………………………………………………………….. 11
2.5 Machine Learning Techniques in Electricity Consumption Prediction …………………………………. 11
2.5.1 Grey Models and their Applications ………………………………………………………………………….. 11
2.5.2 Statistical Models and their Application …………………………………………………………………….. 12
2.5.3 Artificial Intelligence Models …………………………………………………………………………………… 13
2.6 Review of Related Works on Electricity Consumption Prediction ………………………………………. 14
2.7 Summary of Literature Review …………………………………………………………………………………….. 17
CHAPTER THREE RESEARCH METHODOLOGY ……………………………………………………………….. 18
3.1 Introduction ………………………………………………………………………………………………………………. 18
3.2 AUST Campus Information and Data …………………………………………………………………………….. 18
3.3 Weather Conditions at Abuja ……………………………………………………………………………………….. 19

3.4 Electricity Consumption Data for AUST ………………………………………………………………………… 20
3.5 Preliminary Data Analysis …………………………………………………………………………………………… 21
3.6 Demystification of Artificial Neural Networks ………………………………………………………………… 22
3.7 Forecasting with Artificial Neural Networks …………………………………………………………………… 22
3.8 Data Collection ………………………………………………………………………………………………………….. 24
3.8.1 Input Variables………………………………………………………………………………………………………. 24
3.8.2 Output Variables ……………………………………………………………………………………………………. 25
3.9 Data Preprocessing …………………………………………………………………………………………………….. 25
3.10 Model Description of the Network Model for AUST ………………………………………………………. 27
3.11 Training the Network ………………………………………………………………………………………………… 28
3.12 Network Model Parameter Investigation ………………………………………………………………………. 29
3.13 Performance Evaluation Analysis ………………………………………………………………………………… 29
3.14 Implementation of ANN using MATLAB …………………………………………………………………….. 30
3.14.1 Neural Fitting Tool……………………………………………………………………………………………….. 30
3.14.2 Data Selection from the Workspace Area …………………………………………………………………. 31
3.14.3 Data Validation and Testing Pane ……………………………………………………………………………. 31
3.14.4 Network Architecture Pane ……………………………………………………………………………………. 32
3.14.5 Network Training Pane …………………………………………………………………………………………. 33
3.14.6 Network Evaluation Pane ………………………………………………………………………………………. 34
3.14.7 Application Deployment Pane ………………………………………………………………………………… 34
3.14.8 Results Pane ………………………………………………………………………………………………………… 35
CHAPTER FOUR RESULTS AND DISCUSSION …………………………………………………………………… 36
4.1 Introduction ………………………………………………………………………………………………………………. 36
4.2 Performance and Comparisons of the Models ………………………………………………………………….. 36
4.3 Validation and Testing Results ……………………………………………………………………………………… 38
4.4 Prediction of Electricity Consumption with the Built Models …………………………………………….. 39
CHAPTER FIVE CONCLUSION AND FUTURE WORK ………………………………………………………… 41
5.1 Conclusion ……………………………………………………………………………………………………………….. 41
5.2 Future Work ……………………………………………………………………………………………………………… 42
REFERENCES ……………………………………………………………………………………………………………………. 43
APPENDIX ………………………………………………………………………………………………………………………… 48

 

 

CHAPTER ONE

INTRODUCTION
1.1 Background of Study
For any nation to be identified as being extremely industrialized, social, economic and industrial
development must exist. Energy Consumption has become a prime focus in global discussions towards
ensuring sustainable development.
Recent studies have shown that in many parts of the world, energy consumption of buildings exceeds that
of other sectors, including transportation and industries. For example, in the Nigeria, residential buildings
consume as much as 77.8%, while transportation, industries and others account for the rest.
Figure1.1: Schematic Representation of Nigeria Energy Consumption by Sector
Source (Energypedia, 2012) 1 Ktoe (thousand tonnes of oil equivalent) = 11630000 kW/h

In Nigeria, electricity is one of the oldest forms of energy available for daily activities. It is also,
unfortunately, in too short supply to meet the demand of an ever-increasing population. This is largely due
to inadequate planning (Kofoworola, 2003).
Arimah (1993) gave an overview of the current situation of the Nigerian electricity industry where he
mentioned that it is beset with several serious technical, managerial, personnel, financial and logistical
problems. Moreover, the demand for electricity has continued to surpass capacity. The end result has been
the delivery of poor and shoddy services which is evidenced by recurrent power failures.
Studies have shown that by following the current energy consumption pattern, the world energy
consumption may increase by more than 50% before 2030 (Suganthi & Samuel, 2012).
Energy consumption forecasting is significant especially for improving the energy performance of
buildings, leading to energy conservation and reducing its environmental impact. However, the energy
system in buildings is quite complex, as the energy types and building types vary greatly. The most
frequently considered building types are offices, residential and institutions.
Few studies have been carried out in this field, especially in educational institutions in Nigeria.
The energy behaviour of a building is influenced by many factors, such as weather conditions, especially
the dry bulb temperature, building construction and thermal property of the physical materials used,
occupancy behaviour, sublevel components, which include lighting systems, heating, ventilating and air
conditioning (HVAC).
Due to the complexity of the energy system, accurate consumption prediction is quite difficult.

In Nigeria, electricity is one of the oldest forms of energy available for daily activities. It is also,
unfortunately, in too short supply to meet the demand of an ever-increasing population. This is largely due
to inadequate planning (Kofoworola, 2003).
Arimah (1993) gave an overview of the current situation of the Nigerian electricity industry where he
mentioned that it is beset with several serious technical, managerial, personnel, financial and logistical
problems. Moreover, the demand for electricity has continued to surpass capacity. The end result has been
the delivery of poor and shoddy services which is evidenced by recurrent power failures.
Studies have shown that by following the current energy consumption pattern, the world energy
consumption may increase by more than 50% before 2030 (Suganthi & Samuel, 2012).
Energy consumption forecasting is significant especially for improving the energy performance of
buildings, leading to energy conservation and reducing its environmental impact. However, the energy
system in buildings is quite complex, as the energy types and building types vary greatly. The most
frequently considered building types are offices, residential and institutions.
Few studies have been carried out in this field, especially in educational institutions in Nigeria.
The energy behaviour of a building is influenced by many factors, such as weather conditions, especially
the dry bulb temperature, building construction and thermal property of the physical materials used,
occupancy behaviour, sublevel components, which include lighting systems, heating, ventilating and air
conditioning (HVAC).
Due to the complexity of the energy system, accurate consumption prediction is quite difficult. conservation. This will help the universities to plan their budgets based on the population by giving an
insight on the amount of electricity that is required.
1.5 Scope of the Work
The research involves developing an intelligent predictive model that will predict accurately the monthly
electricity consumption required in university buildings. The African University of Science and Technology
is being used as a case study for this research. A database was created for the monthly electricity
consumption using the utility bills from 2012 to 2014 when the meter was not installed and a second
database also containing readings from 2015 to 2017, the period the meter was installed.
ANNs are used for the purpose of this research and the results are compared to examine the built model
with a better predictive accuracy.
1.6 Thesis Structure
The thesis is organized into five chapters as follows.
Chapter 1 covers the basic introductory part of the thesis.
Chapter 2 gives an insight into an overview of electricity consumption in Nigeria and institutional buildings,
concepts related to machine learning prediction as well as a review of current and existing literatures.
Chapter 3 discusses the research methodology used as well as the system architecture and description, with
a detailed discussion of the system implementation.
Chapter 4 provides a detailed discussion on the results and system implementation.
Chapter 5 rounds off the research by giving the summary, conclusions and suggestions for future work

 

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