Artificial Intelligence for Renewable Energy systems

Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy system, and most importantly, the paradigm shift from a market-based cost of the commodity to market-based technological advancements. Featuring recent developments and state-of-the-art applications of artificial intelligence in renewable energy systems design, the book emphasizes how AI supports effective prediction for energy generation, electric grid related line loss prediction, load forecasting, and for predicting equipment failure prevention.

Looking at approaches in system modeling and performance prediction of renewable energy systems, this volume covers power generation systems, building service systems and combustion processes, exploring advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms and hybrid mechanisms.

Autorentext
Ashutosh Kumar Dubey is an Associate Professor in the Department of Computer Science and Engineering at Chitkara University, Himachal Pradesh, India. He is also a Postdoctoral Fellow of the Ingenium Research Group Lab, Universidad
de Castilla-La Mancha, Ciudad Real, Spain. Dr. Sushil Kumar Narang is Dean and an Associate Professor in the Department of Computer Science & Engineering at Chitkara University, Rajpura, Punjab since 2019. From 2006-2019, He was head of IT department at SAS Institute of IT & Research, Mohali, Punjab. From 1996-2006 he was Assistant Professor at Department of Computer Science & Applications, MLN College, Yamuna agar, Haryana. He Completed his Ph.D. at Panjab University, Chandigarh. His Research on Feature Extraction and Neural Network Classifiers for Optical Character Recognition for Good quality handwritten Gurmukhi and Devnagari Characters” focused on various image processing, machine as well as deep learning algorithms. His research interests lie in the area of programming languages, ranging from theory to design to implementation, Image Processing, Data Analytics and Machine Learning. He has collaborated actively with researchers in several other disciplines of computer science, particularly Machine Learning on real world use cases.Dr. Abhishek Kumar is Assistant Director and Associate Professor in the Department of Computer Science and Engineering at Chandigarh University, Punjab, India. He completed his PhD in computer science at the University of Madras (India), and previously worked as a post-doctorate fellow in computer science at Ingenium Research Group, based at the Universidad de Castilla-La Mancha in Spain. He has been teaching in academia for more than 13 years, and has over 160 publications in peer reviewed national and international journals, books, and conferences. His research area includes artificial intelligence, renewable energy applications, image processing, computer vision, data mining, and machine learning.

Dr. Vicente García-Díaz is a Software Engineer and has a PhD in Computer Science. He is an Associate Professor in the Department of Computer Science at the University of Oviedo. He is also part of the editorial and advisory board of several journals and has been editor of several special issues in books and journals. He has supervised 80+ academic projects and published 80+ research papers in journals, conferences and books. His research interests include decision support systems, Domain-Specific languages and eLearning.Dr. Arun Lal Srivastav is an Associate Professor in the Department of Applied Sciences at Chitkara University, Himachal Pradesh, India.

Klappentext
Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy system, and most importantly, the paradigm shift from a market-based cost of the commodity to market-based technological advancements. Featuring recent developments and state-of-the-art applications of artificial intelligence in renewable energy systems design, the book emphasizes how AI supports effective prediction for energy generation, electric grid related line loss prediction, load forecasting, and for predicting equipment failure prevention.

Looking at approaches in system modeling and performance prediction of renewable energy systems, this volume covers power generation systems, building service systems and combustion processes, exploring advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms and hybrid mechanisms.

Inhalt

1. Current State of energy systems
   
   2. Artificial Intelligence and Machine Learning implications to energy systems
   3. Weather forecasting using Artificial Intelligence
   4. Intelligent Energy storage
   5. Modelling and Simulation of Power Electronic Circuits
   6. Control methods in Renewable energy systems
   7. Role of Artificial Intelligence in Power Quality Management and Stability Analysis
   8. Integration of microgrids
   9. Rooftop photovoltaic systems
   10. Biomass and biogas
   11. Renewable energy systems and technologies education
   12. Evolutionary Intelligence in Renewable energy
   13. Smart Energetic Management
   14. RnE: Renewable Energetic Systems
   15. Energy efficient lighting systems
   16. Scope of Artificial Intelligence based solar energy system
   17. Role of Artificial Intelligence in environmental sustainability
   18. Integration of Artificial Intelligence with biomethanation
   19. Hybrid renewable energy system and Artificial Intelligence
   20. Renewable energy and sustainable developments