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.
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.
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
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 a professor and post-doctorate
fellow in computer science at Ingenium Research Group, based at
Universidad De Castilla-La Mancha in Spain. He has been teaching in
academia for more than 8 years, and published more than 50 articles
in reputed, peer reviewed national and international journals,
books, and conferences. His research area includes artificial
intelligence, 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.
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