Artificial Intelligence for Renewable Energy systems
- 1st Edition - August 1, 2022
- Imprint: Woodhead Publishing
- Editors: Ashutosh Kumar Dubey, Sushil Narang, Abhishek Kumar, Vicente García-Díaz, Arun Lal Srivastav
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 3 9 6 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 6 6 1 - 6
Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy sy… Read more
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Request a sales quoteArtificial 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.
- Includes real-time applications that illustrates artificial intelligence and machine learning for various renewable systems
- Features a templated approach that can be used to explore results, with scientific implications followed by detailed case studies
- Covers computational capabilities and varieties for renewable system design
Both renewable energy researchers and practicing engineers, working in the fields of AI and Renewable Energy. Mostly research with up-and-coming practical applications included
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- 1. Techno-economic study of off-grid renewable energy systems in Pindar and Saryu Valleys, Uttarakhand, India
- 1.1. Introduction
- 1.2. Review of literature
- 1.3. Study area and methodology
- 1.4. Assessment of components
- 1.5. Techno-economic feasibility of off-grid renewable energy system in Khati village, Pindar Valley
- 1.6. Techno-economic feasibility of off-grid renewable energy system in Jhuni
- 1.7. Summary and conclusion
- 2. Analyzing predictive ability of artificial neural network–based short-term forecasting algorithms for temperature and wind speed
- 2.1. Introduction
- 2.2. Literature
- 2.3. Methods
- 2.4. Results
- 2.5. Discussion
- 2.6. Limitations and future predictions
- 2.7. Conclusion
- 3. Role of renewable energy in attaining sustainable development
- 3.1. Introduction
- 3.2. Types of energy sources
- 3.3. Impact of conventional energy sources: a brief outlook
- 3.4. Renewable and clean energy: the sustainable way
- 3.5. Digitalizing and applying new technologies to the sustainable energy sector
- 3.6. Issues and challenges
- 3.7. Conclusion
- 4. Biogas from waste and nanoparticles as renewable energy: current status and outlook
- 4.1. Introduction
- 4.2. Renewable energy resources
- 4.3. Waste as a renewable source
- 4.4. Nanoparticles for biomass conversion
- 4.5. Advantages and disadvantages of biogas technologies
- 4.6. Future predictions
- 4.7. Conclusion
- 5. Microbial fuel cells: potentially sustainable technology for bioelectricity production using palm oil mill effluents
- 5.1. Introduction
- 5.2. Environmental impacts of palm oil processing wastewater
- 5.3. Architectural designs of microbial fuel cells
- 5.4. Bioelectricity potentials from palm oil mill effluents
- 5.5. Intrinsic and environmental factors influencing bioelectricity production from palm oil mill effluents using microbial fuel cell technology
- 5.6. Essential microorganisms for palm oil mill effluent treatment and bioelectricity production
- 5.7. Sustainability of palm oil mill effluents for bioelectricity production
- 5.8. Development of smart energy generation and distribution system using artificial intelligence
- 5.9. Limitations
- 5.10. Conclusion
- 6. Applying artificial intelligence to predict green concrete compressive strength
- 6.1. Introduction
- 6.2. Research methodology and literature review
- 6.3. Current trends and applications
- 6.4. Comparative study based on results or case study
- 6.5. Discussion
- 6.6. Limitations and future perspectives
- 6.7. Conclusion
- 7. Case study analysis of solar tree for public spaces
- 7.1. Introduction
- 7.2. Proposed system
- 7.3. Case studies
- 7.4. Experimental results
- 7.5. Conclusion and future scope
- 8. Recent advances in the production of renewable biofuels using microalgae
- 8.1. Introduction
- 8.2. Microalgae biomass harvesting
- 8.3. Microalgae for biofuel production
- 8.4. Methods for extracting biofuels using microalgae
- 8.5. Challenges ahead
- 8.6. Machine learning techniques in biofuel production
- 8.7. Conclusion and future directions
- 9. Artificial intelligence and technology in weather forecasting and renewable energy systems: emerging techniques and worldwide studies
- 9.1. Introduction
- 9.2. Literature review
- 9.3. Current trends
- 9.4. Discussion
- 9.5. Limitations and future predictions
- 9.6. Conclusion
- 10. Different normalization techniques as data preprocessing for one step ahead forecasting of solar global horizontal irradiance
- 10.1. Introduction
- 10.2. Literature review
- 10.3. Materials and methods
- 10.4. Results
- 10.5. Discussion
- 10.6. Limitations and future predictions
- 10.7. Conclusion
- 11. Artificial intelligence-driven power demand estimation and short-, medium-, and long-term forecasting
- 11.1. Introduction
- 11.2. Load estimation challenges
- 11.3. Factors influencing aggregate demand
- 11.4. Literature
- 11.5. Methods
- 11.6. Results
- 11.7. Discussion
- 11.8. Limitations and future predictions
- 11.9. Conclusion
- 12. Challenges and remediation for global warming to achieve sustainable development
- 12.1. Introduction
- 12.2. Literature review
- 12.3. Recent advances in CO2 capture and utilization
- 12.4. Limitations and outlook
- 12.5. Conclusion
- 13. Utilizing artificial intelligence for environmental sustainability
- 13.1. Introduction
- 13.2. Artificial intelligence—the game-changer
- 13.3. Artificial intelligence and nature: better together for enabling environmental sustainability
- 13.4. Unintended consequences of artificial intelligence
- 13.5. Challenges of using artificial intelligence in environmental sustainability
- 13.6. Conclusion
- 14. Alleviating biogas generation with waste biomass: a renewable way forward?
- 14.1. Introduction
- 14.2. General outline and potential of waste biomass
- 14.3. Historical and chemical standpoints of anoxygenic acquisition for microbiological gas genesis
- 14.4. Microbial community analysis of anaerobic digester in biogas production
- 14.5. Biogas production pretreatment avenues
- 14.6. Improving biogas production through genetic and metabolic engineering approaches
- 14.7. Future outlook
- 15. Renewable energy and sustainable development: A global approach towards artificial intelligence
- 15.1. Introduction
- 15.2. Recent sustainable development using renewable energy technology
- 15.3. Artificial intelligence and sustainable development
- 15.4. Renewable energy for sustainable rural development: synergies and mismatches
- 15.5. Biomass resource assessments
- 15.6. Case studies
- 15.7. Conclusion and future perspectives
- 16. Data-driven predictive model development for efficiency and emission characteristics of a diesel engine fueled with biodiesel/diesel blends
- 16.1. Introduction
- 16.2. Literature
- 16.3. Methods
- 16.4. Results and discussion
- 16.5. Limitations and future perspectives
- 16.6. Conclusion
- 17. FWS-DL: forecasting wind speed based on deep learning algorithms
- 17.1. Introduction
- 17.2. Contributions to existing work
- 17.3. Literature review
- 17.4. Materials and methods
- 17.5. Methods
- 17.6. Gated recurrent units
- 17.7. Bidirectional long short-term memory networks
- 17.8. Convolutional neural network with long short-term memory
- 17.9. Results and discussion
- 17.10. Limitations
- 17.11. Conclusion and future work
- Index
- Edition: 1
- Published: August 1, 2022
- No. of pages (Paperback): 406
- No. of pages (eBook): 406
- Imprint: Woodhead Publishing
- Language: English
- Paperback ISBN: 9780323903967
- eBook ISBN: 9780323906616
AD
Ashutosh Kumar Dubey
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.
Affiliations and expertise
Department of Computer Science and Engineering, Institute of Engineering and Technology, Chitkara University, IndiaSN
Sushil Narang
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.
Affiliations and expertise
Dean and Associate Professor, Department of Computer Science and Engineering at Chitkara University, Rajpura, Punjab, IndiaAK
Abhishek Kumar
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.
Affiliations and expertise
Professor, Post-doctorate Fellow, Ingenium Research Group, Universidad De Castilla-La Mancha, SpainVG
Vicente García-Díaz
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.
Affiliations and expertise
Associate Professor, Department of Computer Science, University of Oviedo, SpainAS
Arun Lal Srivastav
Dr. Arun Lal Srivastav is an Associate Professor in the Department of Applied Sciences at Chitkara University, Himachal Pradesh, India.
Affiliations and expertise
Chitkara University, Himachal Pradesh, Solan, IndiaRead Artificial Intelligence for Renewable Energy systems on ScienceDirect