Chapter ONE

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

Chapter TWO

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

Chapter THREE

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

Chapter FOUR

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

Chapter FIVE

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