Short-term electric power forecast in the nigerian power system using artificial neural network

Chapter ONE

INTRODUCTION

• 1.1Introduction
• 1.2Background of study
• 1.3Problem Statement
• 1.4Objective of study
• 1.5Limitation of study
• 1.6Scope of study
• 1.7Significance of study
• 1.8Structure of the research
• 1.9Definition of terms

Chapter TWO

LITERATURE REVIEW

• 2.1Overview of Short-term electric power forecasting
• 2.2Historical perspective on electric power forecasting
• 2.3Methods used in electric power forecasting
• 2.4Artificial Neural Networks (ANN) in power system forecasting
• 2.5Machine Learning in electric power forecasting
• 2.6Time series analysis in power system forecasting
• 2.7Accuracy metrics in power forecasting
• 2.8Challenges in short-term electric power forecasting
• 2.9Innovations in power system forecasting
• 2.10Future trends in electric power forecasting

Chapter THREE

RESEARCH METHODOLOGY

• 3.1Research Methodology Overview
• 3.2Research Design
• 3.3Data Collection Methods
• 3.4Sampling Techniques
• 3.5Data Analysis Procedures
• 3.6Model Development Process
• 3.7Model Validation Techniques
• 3.8Ethical Considerations

Chapter FOUR

DATA PRESENTATION AND ANALYSIS

• 4.1Data Analysis and Results Overview
• 4.2Descriptive Analysis of Power Data
• 4.3Neural Network Model Performance Evaluation
• 4.4Comparison of Different Forecasting Methods
• 4.5Impact of Input Variables on Forecast Accuracy
• 4.6Error Analysis and Residuals Examination
• 4.7Discussion on Forecasting Outcomes
• 4.8Recommendations for Future Research

Chapter FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

• 5.1Summary of Findings
• 5.2Conclusion
• 5.3Implications of Study
• 5.4Contributions to the Field
• 5.5Limitations and Future Research Directions