This book provides a comprehensive overview of the role of modeling in advancing perovskite solar cell technology and its implications for the future of renewable energy. It encompasses various aspects of perovskite solar cell modeling, including computational modeling and simulation techniques, experimental validation methods, optimization strategies, and performance evaluation metrics.
Features:
• Discusses the basic principles, working mechanisms, materials, and designing approaches related to the implementation of perovskite solar cells.
• Covers electron and hole transport models, computational approaches to charge transport, and transport in different perovskite structures.
• Illustrates the crystal structure, composition, optical and electronic properties, stability, and degradation mechanisms of perovskite materials.
• Explains tandem solar cell design principles, interface engineering for tandems, and stability challenges in tandem solar cells.
• Explores the performance parameters related to perovskite solar cells and the implementation of such devices.
It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electrical and communications engineering, energy engineering, renewable energy, and computer science and engineering.
Table of Contents:
Chapter 1. An Introduction to the Solar Energy: A Step Towards Sustainability. Chapter 2. Fundamentals of Perovskite Materials. Chapter 3. Technology Advancements in Solar Cells: A Summary. Chapter 4. Recent Advances in Perovskite Tandem Solar Cells for Enhanced Solar Efficiency. Chapter 5. Modelling Techniques of Perovskite Solar Cells. Chapter 6. Modeling the Future of Renewable Energy: Machine Learning in Solar Energy Prediction. Chapter 7. Optimizing Hybrid Electric Vehicle Performance by Deep Learning for Power Distribution and Regenerative Braking Prediction in Urban Driving Conditions. Chapter 8. Optimization of Power for Solar Panel Optimizer Using Different FPGAs. Chapter 9. Advancing Solar Energy with Machine Learning, Perovskite Technology, and Smart Data Systems. Chapter 10. Machine Learning for Performance Prediction and Optimization. Chapter 11. Machine Learning Applications in Solar Energy: Predicting Performance and Efficiency. Chapter 12. A Novel Hybrid LSTM-XGBoost Model for Enhanced Solar PV Power Generation Forecasting. Chapter 13. A Comprehensive Review of Cybersecurity Challenges in Solar Grids. Chapter 14. Harnessing Machine Learning for Solar Energy Forecasting: Advancing Perovskite Solar Cells and Renewable Energy Solutions. Chapter 15. Toward Secure Solar Energy Systems: A Cyber Perspective. Chapter 16. Thermal and Power Efficient Hardware Design of Solar Panel on Reconfigurable Architecture. Chapter 17. Solar Charge Controller Design on FPGA. Chapter 18. Exploring the Role of Solar Energy in Advancing Agricultural Practices. Chapter 19. Machine Learning in Solar Energy Prediction. Chapter 20. Real-Time Solar Panel Performance Monitoring and Energy Forecasting. Chapter 21. Solar Energy to Sustainable Development Goals: A Case Study. Chapter 22. Advancements and Challenges in All-Perovskite Tandem Solar Cells: A Critical Review
