AI-Powered IoT in the Energy Industry : Digital Technology and Sustainable Energy Systems / / edited by S. Vijayalakshmi, Savita ., Balamurugan Balusamy, Rajesh Kumar Dhanaraj

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
• Edizione: 1st ed. 2023.
• Descrizione fisica: 1 online resource (318 pages)
• Disciplina: 004.678
• Soggetto topico: Electric power distribution; Renewable energy sources; Cooperating objects (Computer systems); Electric power production; Energy Grids and Networks; Renewable Energy; Cyber-Physical Systems; Electrical Power Engineering
• Persona (resp. second.): VijayalaṭcumiCa; BalusamyBalamurugan; DhanarajRajesh Kumar
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: AI and ML Towards Sustainable Solar Energy -- AI and Intermittency Management of Renewable Energy -- AI Impact on Energy and Utilities -- Energy Intelligence – The Smart Grid Perspective -- IoT Towards Leveraging Renewable Energy -- IoT Contribution in Construct of Green Energy -- IoT, Smart Grids, and Big Data – Renewable Energy Insights -- IoT Infrastructure to Energize Electromobility -- Building Sustainable Charging Infrastructure – Smart Solutions -- Biomass Renewable Energy: Introduction and Application of AI and IoT -- Modernization of Rural Electric Infrastructure -- AI and IoT in Improving Resilience of Smart Energy Infrastructure -- Empowering Renewable Energy Using Internet of Things -- Role of Artificial Intelligence in Renewable Energy -- IoT and Sustainable Energy System: Risk and Opportunity -- Powering the Geothermal Energy with AI, IoT, and ML.
• Sommario/riassunto: AI-Powered IoT in the Energy Industry: Digital Technology and Sustainable Energy Systems looks at opportunities to employ cutting-edge applications of artificial intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) in designing and modeling energy and renewable energy systems. The book's main objectives are to demonstrate how big data can help with energy efficiency and demand reduction, increase the usage of renewable energy sources, and assist in transitioning from a centralized system to a distributed, efficient, and embedded energy system. Contributions cover the fundamentals of the renewable energy sector, including solar, wind, biomass, and hydrogen, as well as building services and power generation systems. Chapters also examine renewable energy property prediction methods and discuss AI and IoT prediction models for biomass thermal properties. Covers renewable energy sector fundamentals; Explains the application of big data in distributed energy domains; Discusses AI and IoT prediction methods and models.
• Titolo autorizzato: AI-powered IoT in the energy industry
• ISBN: 9783031150449; 9783031150432
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910686478203321
• Lo trovi qui: Univ. Federico II
• Serie: Power Systems, . 1860-4676