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Machine Learning Applications in Renewable Energy

Language EnglishEnglish
Book Hardback
Book Machine Learning Applications in Renewable Energy Namrata Manohar
Libristo code: 47057280
Publishers Springer, Berlin, November 2024
This book presents the need for Renewable Energy Technologies (RET) in the context of providing a so... Full description
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This book presents the need for Renewable Energy Technologies (RET) in the context of providing a solution for the depletion of conventional resources, protecting the environment and enhancing the economic situation of a country by way of providing employment opportunities for many people may be as employees in various roles or initiating their own enterprise. The book includes statistics on energy consumption changes over the past few decades from conventional to renewable energies. The future scenario of energy in view of technological advancements and the employment status past, present and future is indicated. The need and importance of standards for the efficient operation of renewable energy systems are explained. The various modern technologies that are enabling the successful implementation of RET are presented. The role of the public and government and the various financial schemes governments provide is highlighted. A few modern applications and those under development would enhance the standard of living. The statistics and situation of the various aspects in the wake of the COVID-19 pandemic before, during and future effects are discussed, for the overall benefit of one and all. The various methods of a cost analysis of a project are indicated. Solar system components and the cost estimation of the solar power system in the present-day market status are provided. The various grid integration issues have been discussed.

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About the book

Full name Machine Learning Applications in Renewable Energy
Language English
Binding Book - Hardback
Date of issue 2025
Number of pages 242
EAN 9789819799381
Libristo code 47057280
Publishers Springer, Berlin
Weight 543
Dimensions 155 x 235
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