Advances of Artificial Intelligence in a Green Energy Environment (eBook, ePUB)

Redaktion: Vasant, Pandian; Weber, Gerhard-Wilhelm; Munapo, Elias; Thomas, Joshua

• Format: ePub

Produktbeschreibung

Advances of Artificial Intelligence in a Green Energy Environment reviews the new technologies in intelligent computing and AI that are reducing the dimension of data coverage worldwide. This handbook describes intelligent optimization algorithms that can be applied in various branches of energy engineering where uncertainty is a major concern.

Including AI methodologies and applying advanced evolutionary algorithms to real-world application problems for everyday life applications, this book considers distributed energy systems, hybrid renewable energy systems using AI methods, and new opportunities in blockchain technology in smart energy.

Covering state-of-the-art developments in a fast-moving technology, this reference is useful for engineering students and researchers interested and working in the AI industry.

• Looks at new techniques in artificial intelligence (AI) reducing the dimension of data coverage worldwide
• Chapters include AI methodologies using enhanced hybrid swarm-based optimization algorithms
• Includes flowchart diagrams for exampling optimizing techniques

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 414
• Erscheinungstermin: 20. Mai 2022
• Englisch
• ISBN-13: 9780323885744
• Artikelnr.: 64081081

Autorenporträt

Pandian Vasant is a Research Associate at MERLIN Research Centre, TDTU in Vietnam. He holds a PhD in Computational Intelligence, an MSc in Engineering Mathematics, and a BSc in Mathematics. His research interests include soft computing, hybrid optimization, holistic optimization, innovative computing, and applications.

J. Joshua Thomas is an Associate Professor at UOW Malaysia KDU Penang University College. He obtained his PhD (Intelligent Systems Techniques) from University Sains Malaysia, Penang, and master's degree from Madurai Kamaraj University, India. He is working with deep learning algorithms, specially targeting on graph convolutional neural networks and bidirectional recurrent neural networks for drug target interaction and image tagging with embedded natural language processing. His work involves experimental research with software prototypes and mathematical modeling and design.

Inhaltsangabe

1. Application of some ways to intensify the process of anaerobic bioconversion of organic matter
2. Disasters impact assessment based on socioeconomic approach
3. Uninterruptible power supply system of the consumer, reducing peak network loads
4. Optimization of the anaerobic conversion of green biomass into volatile fatty acids for further production of high-calorie liquid fuel
5. Life cycle cost and life cycle assessment: an approximation to understand the real impacts of the Electricity Supply Industry
6. Comparison of open access multiobjective optimization software tools for standalone hybrid renewable energy systems
7. Optimization of the process of anaerobic processing of organic waste in biogas plants through the use of a vortex layer apparatus
8. Search of regularities in data: optimality, validity, and interpretability
9. Artificial intelligence techniques for modeling of wind energy harvesting systems: a comparative analysis
10. Human paradigm and reliability for aggregate production planning under uncertainty
11. Artificial intelligenceebased intelligent geospatial analysis in disaster management
12. Optimizing the daily use of limited solar panels in closely located rural schools in Zimbabwe
13. Review on recent implementations of multiobjective and multilevel optimization in sustainable energy economics
14. Hybrid optimization and artificial intelligence applied to energy systems: a review
15. A brief literature review of quantitative models for sustainable supply chain management
16. Optimized designing spherical void structures in 3D domains
17. Swarm-based intelligent strategies for charging plug-in hybrid electric vehicles