MOMENTAN AUSVERKAUFT
Über dieses Produkt
Product Identifiers
PublisherCRC Press LLC
ISBN-101032299789
ISBN-139781032299785
eBay Product ID (ePID)17059116949
Product Key Features
TopicEngineering (General)
Book TitleMachine Learning and Internet of Things in Solar Power Generation
Publication Year2023
Number of Pages232 Pages
LanguageEnglish
IllustratorYes
GenreTechnology & Engineering
AuthorPrabha Umapathy
FormatHardcover
Additional Product Features
LCCN2022-059027
Dewey Edition23/eng/20230113
Dewey Decimal621.310285
SynopsisThe book investigates various MPPT algorithms, and the optimization of solar energy using machine learning and deep learning. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electronics and communication, computer, and environmental. This book: Discusses data acquisition by the internet of things for real-time monitoring of solar cells. Covers artificial neural network techniques, solar collector optimization, and artificial neural network applications in solar heaters, and solar stills. Details solar analytics, smart centralized control centers, integration of microgrids, and data mining on solar data. Highlights the concept of asset performance improvement, effective forecasting for energy production, and Low-power wide-area network applications. Elaborates solar cell design principles, the equivalent circuits of single and two diode models, measuring idealist factors, and importance of series and shunt resistances. The text elaborates solar cell design principles, the equivalent circuit of single diode model, the equivalent circuit of two diode model, measuring idealist factor, and importance of series and shunt resistances. It further discusses perturb and observe technique, modified P&O method, incremental conductance method, sliding control method, genetic algorithms, and neuro-fuzzy methodologies. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electronics and communication, computer, and environmental.
LC Classification NumberTK1087.M344 2023
