![]() | CVMIR-2023: Computer vision and machine intelligence for Renewable Energy |
Submission link | https://easychair.org/conferences/?conf=cvmir2023 |
Abstract submission deadline | August 1, 2023 |
Acceptance/rejection notification (Abstract) | August 17, 2023 |
Full paper submission deadline | October 3, 2023 |
Acceptance/rejection/revision notification (Full chapter) | October 31, 2023 |
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
- Introduction
- Literature
- Methods
- Results
- Discussion
- Limitations
- Conclusion and future work
References
Chapter based on technological insight but without results
Abstract
Keywords
- Introduction
- Literature
- Current Trends and Applications
- Comparative study based on results or Case Study
- Discussion
- Limitations
- 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:
- Computer Vision and AI for Renewable Energy
- Overview of Renewable Energy Sources: Technologies and Applications
- Image Acquisition and Processing Techniques for Renewable Energy: From Sensors to Images
- AI for Renewable Energy: Strategies and Techniques
- AI for Renewable Energy: Fundamentals and Applications
- Recurrent Neural Networks for Renewable Energy: Modeling and Optimization
- Generative Adversarial Networks for Renewable Energy: Synthesizing and Enhancing Data
- Transfer Learning for Renewable Energy: Fine-tuning and Domain Adaptation
- Semantic Segmentation for Renewable Energy: Segmentation and Classification of Renewable Energy Images
- Instance Segmentation for Renewable Energy: Accurate Detection and Segmentation of Renewable Energy Assets
- Classification Techniques for Renewable Energy: Identifying Renewable Energy Sources and Features
- Computer vision-based regression Techniques for Renewable Energy: Predicting Energy Output and Performance
- Anomaly Detection for Renewable Energy: Identifying and Diagnosing Faults and Anomalies in Renewable Energy Systems
- Predictive Maintenance for Renewable Energy: Proactive Maintenance and Asset Management Strategies
- Wind Power Prediction using Computer Vision and Machine Intelligence: Modeling and Forecasting Wind Energy Production
- Solar Power Prediction using Computer Vision and Machine Intelligence: Predicting and Optimizing Solar Energy Generation
- Wave Energy Prediction using Computer Vision and Machine Intelligence: Modeling and Simulation of Wave Energy Converters
- Tidal Energy Prediction using Computer Vision and Machine Intelligence: Analysis and Optimization of Tidal Energy Systems
- Bioenergy Prediction using Computer Vision and Machine Intelligence: Modeling and Optimization of Bioenergy Production
- Energy Storage using Computer Vision: Control and Optimization of Energy Storage Systems
- Optimization of Renewable Energy Systems using Computer Vision: Multi-objective Optimization and Decision-making
- 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