Machine Learning and Computer Vision for Renewable Energy
As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.
1145561003
Machine Learning and Computer Vision for Renewable Energy
As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.
225.0 In Stock
Machine Learning and Computer Vision for Renewable Energy

Machine Learning and Computer Vision for Renewable Energy

Machine Learning and Computer Vision for Renewable Energy

Machine Learning and Computer Vision for Renewable Energy

Overview

As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.

Product Details

ISBN-13: 9798369347041
Publisher: IGI Global
Publication date: 05/01/2024
Pages: 356
Product dimensions: 8.50(w) x 11.00(h) x 0.74(d)

About the Author

Pinaki Pratim Acharjya earned his doctorate in philosophy in 2016 from National Institute of Technology, Agartala, Tripura, India. He is currently employed as a professor in the department of computer science and Engineering, Haldia Institute of Technology, Haldia, West Bengal, India. In several AICTE-approved engineering institutions across India, he has more than 15 years of teaching experience and more than fourteen years of research experience. In numerous national and international journals, conferences, books (written/edited), and book chapters, he has published more than hundred research publications. He is currently conducting research in the fields of Deep Learning, Machine Learning, Data Analytics, Blockchain Technology, Digital Image Processing, Computer Vision, and Artificial Intelligence.

Santanu Koley earned his doctorate in philosophy (PhD) from CSJM University in Kanpur, Uttar Pradesh, India in 2013, and he is currently employed as a professor in the department of computer science and engineering at Haldia Institute of Technology in Haldia, West Bengal, India. In addition to sixteen years of teaching experience, he has more than fourteen years of research experience from several AICTE-approved engineering colleges across India. Dr. Koley has published more than 30 research papers in journals and conferences from throughout the nation and the world. The areas of cloud computing, digital image processing, artificial intelligence, and machine learning are where he is currently concentrating his research efforts.

Subhabrata Barman is an Assistant Professor with the Department of Computer Science & Engineering, Haldia Institute of Technology, West Bengal, India. His research interests are in the field of Wireless Networks, Computational Intelligence, Remote Sensing and Geo-Informatics, Parallel and Grid Computing. He has published research papers at various International and National Journals and Conferences. He is a Professional Member of IEEE, IACSIT, IAENG and a reviewer of International Journal of Wireless Networks (Springer).
From the B&N Reads Blog
Page 1 of

Customer Reviews