Computer Vision and Machine Intelligence for Renewable Energy Systems

Computer Vision and Machine Intelligence in Renewable Energy Systems, the first release in Elsevier's cutting-edge new series, Advances in Intelligent Energy Systems, offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration. The book equips readers with a variety of essential tools and applications, outlining the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence and breaking down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures.Other sections offer case studies and applications to a wide range of renewable energy source and the future possibilities of the technology. This book provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids. Provides a sorely-needed primer on the opportunities of computer vision techniques for renewable energy systemsBuilds knowledge and tools in a systematic manner, from fundamentals to advanced applicationsIncludes dedicated chapters with case studies and applications for each sustainable energy source INDICE: Part I: Fundamentals of Computer Vision and Machine Learning for Renewable Energy Systems1. Introduction to Computer Vision and AI for Renewable Energy: Challenges and Opportunities2. Overview of Renewable Energy Sources: Technologies and Applications3. Image Acquisition and Processing Techniques for Renewable Energy: From Sensors to Images4. AI for Renewable Energy: Strategies and Techniques5. AI for Renewable Energy: Fundamentals and ApplicationsPart II: Computer Vision Techniques for Renewable Energy Systems6. Recurrent Neural Networks for Renewable Energy: Modeling and Optimization7. Generative Adversarial Networks for Renewable Energy: Synthesizing and Enhancing Data8. Transfer Learning for Renewable Energy: Fine-tuning and Domain Adaptation9. Semantic Segmentation for Renewable Energy: Segmentation and Classification of Renewable Energy Images10. Instance Segmentation for Renewable Energy: Accurate Detection and Segmentation of Renewable Energy Assets11. Classification Techniques for Renewable Energy: Identifying Renewable Energy Sources and Features12. Computer Vision-based Regression Techniques for Renewable Energy: Predicting Energy Output and Performance13. Anomaly Detection for Renewable Energy: Identifying and Diagnosing Faults and Anomalies in Renewable Energy Systems14. Predictive Maintenance for Renewable Energy: Proactive Maintenance and Asset Management Strategies15. Optimization of Renewable Energy Systems using Computer Vision: Multi-objective Optimization and Decision-makingPart III: Renewable Energy Sources and Computer Vision Opportunities16. Wind Power Prediction using Computer Vision and Machine Intelligence: Modeling and Forecasting Wind Energy Production17. Solar Power Prediction using Computer Vision and Machine Intelligence: Predicting and Optimizing Solar Energy Generation18. Wave Energy Prediction using Computer Vision and Machine Intelligence: Modeling and Simulation of Wave Energy Converters19. Tidal Energy Prediction using Computer Vision and Machine Intelligence: Analysis and Optimization of Tidal Energy Systems20. Bioenergy Prediction using Computer Vision and Machine Intelligence: Modeling and Optimization of Bioenergy Production21. Energy Storage using Computer Vision: Control and Optimization of Energy Storage SystemsPart IV: Future Directions22. Future Directions of Computer Vision and AI for Renewable Energy: Trends and Challenges in Renewable Energy Research and Applications

• ISBN: 978-0-443-28947-7
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 300
• Fecha Publicación: 01/10/2024
• Nº Volúmenes: 1
• Idioma: Inglés