Neuware - 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.

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Bibliographic Details

Title: Intelligent Data Analytics for Solar Energy Prediction and Forecasting : Advances in Resource Assessment and PV Systems Optimization
Publisher: Elsevier - Health Sciences Division Jul 2025
Publication Date: 2025
Language: English
ISBN 10: 0443134820
ISBN 13: 9780443134821
Binding: Taschenbuch
Condition: Neu
Dimensions: 229 millimeters width by 152 millimeters height by 0 millimeters depth
Weight: 490 grams

About this title

Synopsis

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

About the Authors

Dr. Amit Kumar Yadav received his B.Tech in Electrical and Electronics Engineering in 2009 from United College of Engineering and Research Naini Allahabad Uttar Pradesh, India, M.Tech. in Power Systems in 2011, and Ph.D. in artificial neural network-based prediction of solar radiation for optimum sizing of photovoltaic systems for power generation in 2016, from the Centre for Energy and Environmental Engineering National Institute of Technology, Hamirpur, Himachal Pradesh, India. Currently, he is faculty in the Electrical and Electronics Engineering Department, National Institute of Technology, Sikkim, India. Dr. Yadav has authored numerous articles in international journals, 10 book chapters, and 12 IEEE conference publications, is an Editorial Board Member of the Turkish Journal of Forecasting, and acts as a reviewer for a number of journals. He received an award as “Best Researcher In Solar Photovoltaic Systems For Maximum Power Generation� in the Research Under Literal Access (RULA) International Awards in 2019. His research interests include Solar Photovoltaics, Engineering Optimization, Artificial Neural Network, Soft Computing, Wind Speed and Solar Radiation Prediction/Forecasting, Solar and Wind Resource Assessment, and Condition Monitoring of Photovoltaic Systems.

Dr. Hasmat Malik received his Diploma in Electrical Engineering from Aryabhatt Govt. Polytechnic Delhi, B.Tech. degree in electrical & electronics engineering from the GGSIP University, Delhi, M.Tech degree in electrical engineering from National Institute of Technology (NIT) Hamirpur, Himachal Pradesh, and Ph.D in power systems from the Electrical Engineering Department, Indian Institute of Technology (IIT) Delhi, India. He is currently a Postdoctoral Scholar at BEARS, University Town, NUS Campus, Singapore, and an Assistant Professor (on-Leave) at the Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology Delhi, India. A member of various societies, Dr. Malik has published over 100 research articles, including papers in international journals, conferences, and book chapters. He was a Guest Editor of Special Issues of the Journal of Intelligent & Fuzzy Systems, in 2018 and 2020. Dr. Malik has supervised 23 postgraduate students and is involved in several large R&D projects. His principal research interests are artificial intelligence, machine learning, and big-data analytics for renewable energy, smart building & automation, condition monitoring, and online fault detection & 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.

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