Intelligent Data Analytics for Solar Energy Prediction and Forecasting (Paperback)
Language: English
Published by Elsevier - Health Sciences Division, Philadelphia, 2025
- Softcover
- New

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Paperback. Intelligent Data Analytics for Solar Energy Prediction and Forecasting: Advances in Resource Assessment and PV Systems O This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller Inventory # 9780443134821
- Title
- Intelligent Data Analytics for Solar Energy Prediction and Forecasting (Paperback)
- Author
- Hasmat Malik
- Publisher
- Elsevier - Health Sciences Division, Philadelphia
- Publication year
- 2025
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0443134820
- ISBN 13
- 9780443134821
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
"Synopsis" may belong to another edition of this title.
About the Author
Hasmat Malik is a Senior Lecturer in the Department of Electrical Power Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia. His research interests include artificial intelligence, machine learning, and big-data analytics applied to renewable energy, smart building automation, condition monitoring, and online fault detection and diagnosis (FDD).
Dr. Majed A. Alotaibi received the B.Sc. degree in electrical engineering from King Saud University, Riyadh, Saudi Arabia in 2010. He obtained his M.A.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Waterloo, Waterloo, Canada in 2014 and 2018, respectively. He is currently an assistant professor in the Department of Electrical Engineering, Vice Dean for Educational and Academic Affairs and the Director of Saudi Electricity Company research chair at King Saud University, Saudi Arabia. Dr. Alotaibi has published over 40 research articles in highly ranked peer reviewed journals and has served as a reviewer for IEEE Transactions on Power Systems and IEEE Transactions on Smart Grids. He has also worked as an electrical design engineer with ABB Saudi Arabia. His research interests include application of artificial intelligence, machine learning and big-data analytics for power system planning, operation, renewable energy modeling, applied optimization and smart grid.
"About the title" may belong to another edition of this title.
Grand Eagle Retail
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AbeBooks seller since October 12, 2005
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