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Intelligent Data Analytics for Solar Energy Prediction and Forecasting

Advances in Resource Assessment and PV Systems Optimization

Language EnglishEnglish
Book Paperback
Book Intelligent Data Analytics for Solar Energy Prediction and Forecasting Hasmat Malik
Libristo code: 43126141
Publishers Elsevier Science, November 2023
Intelligent Data Analytics for Solar Energy Prediction and Forecasting: Advances in Resource Assessm... Full description
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209.37 €
409.49  лв
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Intelligent Data Analytics for Solar Energy Prediction and Forecasting: Advances in Resource Assessment and PV Systems Optimization explores the utilization of advanced neural network, machine learning, and data analytics techniques for solar radiation prediction and solar energy forecasting, supporting installation and maximum power generation. The book addresses key questions relating to the analysis of relevant input variable selection, solar resource assessment, tilt angle calculation, and electrical characteristics of PV modules. This is supported by detailed methods, coding, modelling, and experimental analysis of PV power generation under outdoor conditions. This book is of interest to researchers, scientists, and advanced students across solar energy, renewable energy, electrical engineering, AI and machine learning, computer science and information technology, control engineering, mechanical engineering, and electronics, as well as engineers, R&D professionals, and other industry personnel with an interest in applications of AI, machine learning, and data analytics within solar energy and energy systems more generally. Presents novel intelligent techniques with step-by-step coverage for improved optimum tilt angle calculation for installation of photovoltaic systems Provides coding and modelling for data-driven techniques in prediction and forecasting

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About the book

Full name Intelligent Data Analytics for Solar Energy Prediction and Forecasting
Author Hasmat Malik, Amit Yadav, Majed Alotaibi
Language English
Binding Book - Paperback
Date of issue 2023
Number of pages 350
EAN 9780443134821
Libristo code 43126141
Publishers Elsevier Science
Weight 450
Dimensions 152 x 229

Categories

Economics, Finance, Business & Management > Industry & industrial studies > Energy industries & utilities
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