A PREDICTIVE MODEL FOR ELECTRICITY CONSUMPTION IN UNIVERSITY CAMPUSES USING ARTIFICIAL NEURAL NETWORKS (A CASE STUDY)

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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 201 5–2017. 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

ABSTRACT
LIST OF FIGURES
LIST OF TABLES
LIST OF ABBREVIATIONS

CHAPTER ONE: INTRODUCTION
1.1 Background of Study
1.2 Statement of the Problem
1.3 Aim and Objectives
1.4 Expected Contributions
1.5 Scope of the Work
1.6 Thesis Structure

CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction
2.2 Overview of Electricity Consumption in Nigeria
2.3 Electricity Consumption in Institutional Buildings
2.4 Machine Learning
2.4.1 Supervised Learning
2.4.2 Unsupervised Learning
2.4.3 Reinforcement Learning
2.5 Machine Learning Techniques in Electricity Consumption Prediction
2.5.1 Grey Models and their Applications
2.5.2 Statistical Models and their Application
2.5.3 Artificial Intelligence Models
2.6 Review of Related Works on Electricity Consumption Prediction
2.7 Summary of Literature Review

CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Introduction
3.2 AUST Campus Information and Data
3.3 Weather Conditions at Abuja
3.4       Electricity Consumption Data for AUST
3.5       Preliminary Data Analysis
3.6       Demystification of Artificial Neural Networks
3.7       Forecasting with Artificial Neural Networks
3.8       Data Collection
3.8.1 Input Variables
3.8.2 Output Variables
3.9       Data Preprocessing
3.10 Model Description of the Network Model for AUST
3.11 Training the Network
3.12 Network Model Parameter Investigation
3.13 Performance Evaluation Analysis
3.14 Implementation of ANN using MATLAB
3.14.1  Neural Fitting Tool
3.14.2  Data Selection from the Workspace Area
3.14.3  Data Validation and Testing Pane
3.14.4  Network Architecture Pane
3.14.5  Network Training Pane
3.14.6  Network Evaluation Pane
3.14.7  Application Deployment Pane
3.14.8  Results Pane

CHAPTER FOUR: RESULTS AND DISCUSSION
4.1       Introduction
4.2       Performance and Comparisons of the Models
4.3       Validation and Testing Results
4.4       Prediction of Electricity Consumption with the Built Models

CHAPTER FIVE: CONCLUSION AND FUTURE WORK
5.1       Conclusion
5.2       Future Work
REFERENCES
APPENDIX


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.

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....


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