CVMIR-2023: Computer vision and machine intelligence for Renewable Energy

Call for Elsevier book chapter proposal

Computer vision and machine intelligence for Renewable Energy

Submission Guidelines

Please submit your abstract, which should not exceed 500 words, in Word format to chaptersubmission4@gmail.com by August 1, 2023. Kindly include complete author details, including email addresses, in the submission.

If the abstract is accepted, the authors are required to submit the original full chapter, which should not be simultaneously submitted to any journal, conference, or edited book. The full chapter should be submitted in the following format:

Chapter based on method and results

Abstract

Keywords

1. Introduction
2. Literature
3. Methods
4. Results
5. Discussion
6. Limitations 
7. Conclusion and future work

References

Chapter based on technological insight but without results

Abstract

Keywords

1. Introduction
2. Literature
3. Current Trends and Applications
4. Comparative study based on results or Case Study
5. Discussion
6. Limitations 
7. Conclusion and future work

References

Book summary:

Computer vision (CV) in artificial intelligence extracts meaningful information from digital images/videos, benefiting renewable energy. CV enhances sustainability across industries by predicting factors impacting renewables, improving energy management. It enables accurate inspections, facilitates timely maintenance, and aids predictions related to energy generation, grid, consumption, weather, and equipment failure. This book explores CV applications in renewable energy systems design, including solar/wind energy and power generation, utilizing various CV techniques. It focuses on modeling and performance prediction for energy systems, introducing new dimensions.

Potential topics include, but are not limited to the following:

1. Computer Vision and AI for Renewable Energy
2. Overview of Renewable Energy Sources: Technologies and Applications
3. Image Acquisition and Processing Techniques for Renewable Energy: From Sensors to Images
4. AI for Renewable Energy: Strategies and Techniques
5. AI for Renewable Energy: Fundamentals and Applications
6. Recurrent Neural Networks for Renewable Energy: Modeling and Optimization
7. Generative Adversarial Networks for Renewable Energy: Synthesizing and Enhancing Data
8. Transfer Learning for Renewable Energy: Fine-tuning and Domain Adaptation
9. Semantic Segmentation for Renewable Energy: Segmentation and Classification of Renewable Energy Images
10. Instance Segmentation for Renewable Energy: Accurate Detection and Segmentation of Renewable Energy Assets
11. Classification Techniques for Renewable Energy: Identifying Renewable Energy Sources and Features
12. Computer vision-based regression Techniques for Renewable Energy: Predicting Energy Output and Performance
13. Anomaly Detection for Renewable Energy: Identifying and Diagnosing Faults and Anomalies in Renewable Energy Systems
14. Predictive Maintenance for Renewable Energy: Proactive Maintenance and Asset Management Strategies
15. Wind Power Prediction using Computer Vision and Machine Intelligence: Modeling and Forecasting Wind Energy Production
16. Solar Power Prediction using Computer Vision and Machine Intelligence: Predicting and Optimizing Solar Energy Generation
17. Wave Energy Prediction using Computer Vision and Machine Intelligence: Modeling and Simulation of Wave Energy Converters
18. Tidal Energy Prediction using Computer Vision and Machine Intelligence: Analysis and Optimization of Tidal Energy Systems
19. Bioenergy Prediction using Computer Vision and Machine Intelligence: Modeling and Optimization of Bioenergy Production
20. Energy Storage using Computer Vision: Control and Optimization of Energy Storage Systems
21. Optimization of Renewable Energy Systems using Computer Vision: Multi-objective Optimization and Decision-making
22. Future Directions of Computer Vision and AI for Renewable Energy: Trends and Challenges in Renewable Energy Research and Applications.

Editors:

Dr. Ashutosh Kumar Dubey,  Associate Professor, Senior Member (IEEE and ACM), Department of Computer Science & Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Himachal Pradesh, India

Dr. Arun Lal Srivastav, Assistant Professor, Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India

Dr. Abhishek Kumar, Associate Professor, Senior Member (IEEE), Department of Computer Science & Engineering, Chandigarh University, Punjab, India

Dr. Umesh Chandra Pati, Professor, Senior Member, IEEE, Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, India

Dr. Fausto Pedro García Márquez, Full Professor, Castilla-La Mancha University, Spain

Dr. Vicente García-Díaz, Associate Professor, Department of Computer Science, University of Oviedo, Oviedo, Spain