Samara University Repository
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This is an open electronic archive created to preserve and disseminate the scientific results of our university. The works of researchers, faculty members, and students are available to everyone for academic and research purposes.
The repository contains dissertations, research articles, educational and methodological materials, monographs, and regulatory documents. The collections cover a wide range of fields: from aerospace technologies, engine engineering, and computer science to history, linguistics, mathematics, and ecology. The archive brings together the scientific heritage of the university, including materials prepared during the period of Samara State Aerospace University and Samara State University.
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| Title: | Artificial Intelligence and Internet of Things for Renewable Energy Systems |
| Authors: | Bansal R. C. Hiran K.K. Holm-Nielson J.B. Padmanaban S. Priyadarshi N. |
| Keywords: | big data analytics in energy energy storage fuzzy logic and rule-based systems internet of things machine learning maximum power point tracking photovoltaic systems remote monitoring and control systems renewable energy systems Smart Grid аналитика больших данных в энергетике интернет вещей машинное обучение накопление энергии отслеживание точек максимальной мощности системы возобновляемой энергии системы нечеткой логики и правил системы удаленного мониторинга и управления умная сеть фотоэлектрические системы |
| Issue Date: | 2022 |
| Publisher: | De Gruyter |
| Citation: | Artificial Intelligence and Internet of Things for Renewable Energy Systems / edited by Jens Bo Holm-Nielson [and other]. - Berlin ; Boston : De Gruyter, 2022. - 1 file (353 Mb) (321 p.). - ISBN = 9783110713794, 9783110714043, 9783110714159. - Текст : электронный |
| Abstract: | This book explains the application of Artificial Intelligence and Internet of Things on green energy systems. The design of smart grids and intelligent networks enhances energy efficiency, while the collection of environmental data through sensors and their prediction through machine learning models improve the reliability of green energy systems. В книге рассказывается о применении искусственного интеллекта и интернета вещей в системах экологически чистой энергетики. Проектирование интеллектуальных сетей повышает энергоэффективность, а сбор экологических данных с помощью датчиков и их прогнозирование с помощью моделей машинного обучения повышают надежность систем экологически чистой энергетики. Используемые программы Adobe Acrobat |
| URI: | http://repo.ssau.ru/jspui/handle/123456789/62474 |
| ISBN: | 9783110713794 9783110714043 9783110714159 |
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| Other Identifiers: | 3102607 |
| Appears in Collections: | eBooks |
| File | Size | Format | |
|---|---|---|---|
| 3102607.pdf | 362.36 MB | Adobe PDF | View/Open Request a copy |
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