Machine Learning And Computer Vision For Renewable Energy
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Machine Learning and Computer Vision for Renewable Energy
Author: Acharjya, Pinaki Pratim
language: en
Publisher: IGI Global
Release Date: 2024-05-01
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.
Computer Vision and Machine Intelligence for Renewable Energy Systems
Computer Vision and Machine Intelligence for Renewable Energy Systems offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration.This book equips readers with a variety of essential tools and applications: Part I outlines the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence: minimal computing power needs, speed, and accuracy even with partial data. Part II breaks down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures. Part III offers case studies and applications to a wide range of renewable energy sources, and finally the future possibilities of the technology are considered. The very first book in Elsevier's cutting-edge new series Advances in Intelligent Energy Systems, Computer Vision and Machine Intelligence for Renewable Energy Systems 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 systems - Builds knowledge and tools in a systematic manner, from fundamentals to advanced applications - Includes dedicated chapters with case studies and applications for each sustainable energy source
Sustainable Development through Machine Learning, AI and IoT
This book constitutes the refereed proceedings of the Second International Conference on Sustainable Development through Machine Learning, AI and IoT, ICSD 2024, held in Virtual Event, during April 27–28, 2024. The 38 full papers presented here were carefully reviewed and selected from 167 submissions. These papers have been categorized into the following sections: This volume encompassing a diverse array of topics at the intersection of cutting-edge technologies and practical applications. Each chapter delves into innovative approaches and solutions, providing valuable insights into contemporary challenges and opportunities in various domains. Here, we explore the realms of blockchain, data science, machine learning, artificial intelligence, and more, offering in-depth analyses and practical implementations.