Intelligent Data Analytics for Solar Energy Prediction and Forecasting - Yadav, Amit Kumar; Malik, Hasmat; Alotaibi, Majed A.; - Prospero Internet Bookshop
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Product details:
ISBN13:9780443134821
ISBN10:04431348211
Binding:Paperback
No. of pages:350 pages
Size:229x152 mm
Weight:450 g
Language:English
700
Category:

Intelligent Data Analytics for Solar Energy Prediction and Forecasting

Advances in Resource Assessment and PV Systems Optimization
 
Publisher: Elsevier
Date of Publication:
 
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EUR 170.00
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Long description:

Intelligent Data Analytics for Solar Energy Prediction and Forecasting: Advances in Resource Assessment and PV Systems Optimization explores the utilization of advanced neural networks, machine learning and data analytics techniques for solar radiation prediction, solar energy forecasting, installation and maximum power generation. The book addresses relevant input variable selection, solar resource assessment, tilt angle calculation, and electrical characteristics of PV modules, including detailed methods, coding, modeling and experimental analysis of PV power generation under outdoor conditions. It will be of interest to researchers, scientists and advanced students across solar energy, renewables, electrical engineering, AI, machine learning, computer science, information technology and engineers.

In addition, 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 will find this book to be a welcomed resource.




  • Presents novel intelligent techniques with step-by-step coverage for improved optimum tilt angle calculation for the installation of photovoltaic systems
  • Provides coding and modeling for data-driven techniques in prediction and forecasting
  • Covers intelligent data-driven techniques for solar energy forecasting and prediction
Table of Contents:

PART A: Solar Energy Prediction and Forecasting Resources
1. Intelligent Data Analytics Tools and Techniques
2. Solar Energy Prediction and Forecasting Resource Assessment

PART B: Market Research and Survey of Intelligent Data Analytics for Solar Energy Prediction and Forecasting
3. Intelligent Data Analytics in Solar Irradiance Prediction
4. Intelligent Data Analytics for Tilt Angle Optimization of PV Systems
5. Intelligent Data Analytics for Electrical Characteristics of Solar PV Modules

PART C: Intelligent Data Analytics Methods for Solar Energy Prediction and Forecasting
6. Intelligent Data Analytics for Feature Extraction and Selection in Solar Radiation Prediction and Forecasting
7. Intelligent Data Analytics for Tilt Angle Optimization for Installation of Solar PV Systems for Maximum Power Generation
8. Intelligent Data Analytics to Analyze the Effect of Tilt Angle on Optimum Sizing and Power Generation of Standalone PV Systems
9. ntelligent Data Analytics to Analyze the Optimum Tilt Angle Influences on Grid Connected PV Systems
10. Intelligent Data Analytics for Maximum Power Prediction of Photovoltaic Modules in Outdoor Conditions
11. Intelligent Data Analytics for Daily Array Yield Prediction of Grid-Interactive Solar PV (GISPV) Plants