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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Part 1: Solar energy prediction and forecasting - p. 1 7. Chapter One. Intelligent data analytics tool and techniques - p. 3 8. Chapter Two. Solar energy prediction and forecasting resource assessment - p. 41 9. Part 2: Market research and survey of intelligent data analytics for solar energy prediction and forecasting - p. 61 10. Chapter Three. Market research/survey related to solar radiation prediction - p. 64 11. Chapter Four. Market research/survey related to tilt angle optimization for photovoltaic systems - p. 96 12. Chapter Five. Market research/survey related to electrical characteristics of solar photovoltaic (PV) module - p. 124 13. Part 3: Intelligent data analytics methods for solar energy prediction and forecasting - p. 145 14. Chapter Six. Intelligent data analytics for feature extraction and selection for solar radiation prediction and forecasting - p. 147 15. Chapter Seven. Intelligent Data Analytics for tilt angle optimization for installation of solar PV systems for maximum power generation - p. 165 16. Chapter Eight. Intelligent data analytics to analyze the effect of tilt angle on optimum sizing and power generation of standalone photovoltaic system - p. 201 17. Chapter Nine. Intelligent data analytics to analyze the optimum tilt angle influences on grid-connected PV systems - p. 221 18. Chapter Ten. Intelligent data analytics for maximum power prediction of photovoltaic module under outdoor conditions - p. 243 19. Chapter Eleven. Intelligent data analytics for daily array yield prediction of grid-interactive solar photovoltaic plant - p. 263 20. Index - p. 285
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Part 1: Solar energy prediction and forecasting - p. 1 7. Chapter One. Intelligent data analytics tool and techniques - p. 3 8. Chapter Two. Solar energy prediction and forecasting resource assessment - p. 41 9. Part 2: Market research and survey of intelligent data analytics for solar energy prediction and forecasting - p. 61 10. Chapter Three. Market research/survey related to solar radiation prediction - p. 64 11. Chapter Four. Market research/survey related to tilt angle optimization for photovoltaic systems - p. 96 12. Chapter Five. Market research/survey related to electrical characteristics of solar photovoltaic (PV) module - p. 124 13. Part 3: Intelligent data analytics methods for solar energy prediction and forecasting - p. 145 14. Chapter Six. Intelligent data analytics for feature extraction and selection for solar radiation prediction and forecasting - p. 147 15. Chapter Seven. Intelligent Data Analytics for tilt angle optimization for installation of solar PV systems for maximum power generation - p. 165 16. Chapter Eight. Intelligent data analytics to analyze the effect of tilt angle on optimum sizing and power generation of standalone photovoltaic system - p. 201 17. Chapter Nine. Intelligent data analytics to analyze the optimum tilt angle influences on grid-connected PV systems - p. 221 18. Chapter Ten. Intelligent data analytics for maximum power prediction of photovoltaic module under outdoor conditions - p. 243 19. Chapter Eleven. Intelligent data analytics for daily array yield prediction of grid-interactive solar photovoltaic plant - p. 263 20. Index - p. 285
