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...
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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.
Bibliographische Angaben
- Herausgegeben:Dubey, Ashutosh Kumar; Narang, Sushil; Srivastav, Arun Lal; Kumar, Abhishek; García-Díaz, Vicente
- Verlag: Woodhead Publishing
- EAN: 9780323903967
Autoren-Porträt
Dr. Ashutosh Kumar Dubey received his PhD degree in Computer Science and Engineering from JK Lakshmipat University, Jaipur, Rajasthan, India. He is currently in the department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India. He is also the Senior Member of IEEE and ACM. He has more than 14 years of teaching experience. He has authored a book name Database Management Concepts. He has been associated with many international and national conferences as the Technical Program Committee member. He is also associated as the Editor/Editorial Board Member/ Reviewer of many peer-reviewed journals. His research areas are Data Mining, Health Informatics, Optimization, Machine Learning, Cloud Computing, Artificial Intelligence and Object-Oriented Programming.Dr Arun Lal Srivastav is working as Assistant Professor at Chitkara University, Himachal Pradesh (India). He obtained his Ph.D. from the Indian Institute of Technology (BHU), Varanasi in 2013 followed by Post-Doctoral Research at National Chung Hsing University, Taiwan in 2014. He is currently involved in the teaching of Environmental Science, Environmental Engineering and Disaster Management to the undergraduate engineering students.His research interests include water treatment, river ecosystem, climate change, soil health maintenance, phytoremediation, and waste management. He has published 50 research papers in various prestigious Journals of the Elsevier, Springer, IWA, Taylor and Francis, Wiley, Hindawi, MDPI, Indian Chemical Society along with in prestigious conferences and books. He has also filed 12 patents.
Inhaltsverzeichnis zu „Artificial Intelligence for Renewable Energy systems “
1. Current State of energy systems2. 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
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