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Introduction

In recent years, the use of Artificial Intelligence (AI) in various industries has shown significant growth and potential. One such industry that has benefited greatly from the application of AI is the renewable energy sector. With the increasing emphasis on sustainability and the shift towards renewable sources of energy, accurate forecasting of renewable energy generation has become crucial for efficient energy management. AI techniques such as machine learning and neural networks have proven to be effective in improving the accuracy of renewable energy forecasting.

This thesis aims to explore the application of AI in renewable energy forecasting. The focus will be on how AI techniques can be used to enhance the accuracy and reliability of forecasting methods for renewable energy sources such as solar and wind power. By leveraging the power of AI, renewable energy stakeholders can make more informed decisions regarding energy production, distribution, and consumption.

Chapter 1: Introduction
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 Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of renewable energy forecasting
2.2 Traditional forecasting methods
2.3 AI techniques in energy forecasting
2.4 Machine learning algorithms for energy forecasting
2.5 Neural networks in renewable energy forecasting
2.6 Hybrid models for energy prediction
2.7 Challenges in renewable energy forecasting
2.8 Case studies on AI in energy forecasting
2.9 Research gaps in the field
2.10 Conclusion

Chapter 3: System Design and Methodology
3.1 Research framework
3.2 Data collection and preprocessing
3.3 Feature selection and engineering
3.4 Model selection
3.5 Training and testing
3.6 Evaluation metrics
3.7 Cross-validation techniques
3.8 Performance analysis
3.9 Comparative study
3.10 Conclusion

Chapter 4: System Implementation
4.1 Implementation of AI models
4.2 Integration with existing forecasting systems
4.3 Testing and validation
4.4 Performance optimization
4.5 Scalability and deployment
4.6 User interface design
4.7 Maintenance and updates
4.8 Case studies
4.9 Results and discussion
4.10 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Implications of the study
5.3 Recommendations for future research
5.4 Contribution to the field
5.5 Conclusion

Overall, this thesis will provide a comprehensive overview of the application of AI in renewable energy forecasting and its potential impact on the renewable energy industry. By leveraging the power of AI techniques, renewable energy stakeholders can make more accurate and reliable predictions, leading to improved energy management and sustainability.

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