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- Autorius: Adel Mellit
- Leidėjas: Elsevier Science
- Metai: 2026
- Puslapiai: 300
- ISBN-10: 0443448353
- ISBN-13: 9780443448355
- Kalba: Anglų
Embedded Artificial Intelligence and the Internet of Things for Photovoltaic Systems (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
Embedded Artificial Intelligence and the Internet of Things for Photovoltaic Systems: From Monitoring to Maintenance and Security is a new handbook that explores the concepts of embedded AI (EAI), Internet of Things (IoT), and related techniques, and their role in addressing key problems in PV whilst enabling the transition from laboratory-scale developments to real-world applications. The book offers thorough, detailed coverage of methods and modern, end-to-end solutions to applying EAI techniques in PV systems, including in supervision, fault detection and diagnosis, smart monitoring systems, and predictive maintenance and security, guiding readers from AI model development to deployment. The first four chapters introduce and define key concepts, including photovoltaics, embedded artificial intelligence, edge devices and platforms, and Internet of Things, before an in-depth chapter describes databases and datasets, an essential component in the development of AI and EAI. The final five chapters focus on applications, by outlining key problems and giving case studies that address real-world issues in PV plants. This is a valuable resource to all those with an interest in photovoltaics and AI, particularly those who are looking to effectively apply embedded AI techniques in PV systems, including researchers, advanced students, faculty, engineers, project managers, technicians, R&D, and other industry professionals.
- Autorius: Adel Mellit
- Leidėjas: Elsevier Science
- Metai: 2026
- Puslapiai: 300
- ISBN-10: 0443448353
- ISBN-13: 9780443448355
- Kalba: Anglų
Embedded Artificial Intelligence and the Internet of Things for Photovoltaic Systems: From Monitoring to Maintenance and Security is a new handbook that explores the concepts of embedded AI (EAI), Internet of Things (IoT), and related techniques, and their role in addressing key problems in PV whilst enabling the transition from laboratory-scale developments to real-world applications. The book offers thorough, detailed coverage of methods and modern, end-to-end solutions to applying EAI techniques in PV systems, including in supervision, fault detection and diagnosis, smart monitoring systems, and predictive maintenance and security, guiding readers from AI model development to deployment. The first four chapters introduce and define key concepts, including photovoltaics, embedded artificial intelligence, edge devices and platforms, and Internet of Things, before an in-depth chapter describes databases and datasets, an essential component in the development of AI and EAI. The final five chapters focus on applications, by outlining key problems and giving case studies that address real-world issues in PV plants. This is a valuable resource to all those with an interest in photovoltaics and AI, particularly those who are looking to effectively apply embedded AI techniques in PV systems, including researchers, advanced students, faculty, engineers, project managers, technicians, R&D, and other industry professionals.
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