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230704 ||| eng |
| 020 |
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|a 9783031264962
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| 100 |
1 |
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|a Manshahia, Mukhdeep Singh
|e [editor]
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| 245 |
0 |
0 |
|a Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy
|h Elektronische Ressource
|c edited by Mukhdeep Singh Manshahia, Valeriy Kharchenko, Gerhard-Wilhelm Weber, Pandian Vasant
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| 250 |
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|a 1st ed. 2023
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2023, 2023
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| 300 |
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|a XXII, 285 p. 116 illus., 90 illus. in color
|b online resource
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| 505 |
0 |
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|a Chapter 1. General Approaches to Assessing Electrical Load of Agro-Industrial Complex Facilities When Justifying the Parameters of the Photovoltaic Power System -- Chapter 2. RBFNN for MPPT Controller in Wind Energy Harvesting System -- Chapter 3. Simulation Optimum Performance All-Wheels Plug-In Hybrid Electric Vehicle -- Chapter 4. Artificial Intelligence application to flexibility provision in energy management system: a survey -- Chapter 5. Machine Learning Applications for Renewable Energy Systems -- Chapter 6. New Technologies and Equipment For Smelting Technical Silicon -- Chapter 7. Reconfiguration of distribution network considering photovoltaic system placement based on metaheuristic algorithms -- Chapter 8. Technology of Secondary Cast Polycrystalline Silicon And Its Application In The Production Of Solar Cells -- Chapter 9. Machine Learning Applications for Renewable based Energy Systems -- Chapter 10. Bi-Objective Optimal Scheduling of Smart Homes Appliances using Artificial Intelligence -- Chapter 11. Optimal placement of photovoltaic systems and wind turbines in distribution systems by using Northern Goshawk Optimization algorithm -- Chapter 12. Granulated silicon and thermal energy converters on its basis -- Chapter 13. Security Constrained Unit Commitment with Wind Energy Resource using Universal Generating Function
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| 653 |
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|a Renewable Energy
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Renewable energy sources
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| 700 |
1 |
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|a Kharchenko, Valeriy
|e [editor]
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| 700 |
1 |
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|a Weber, Gerhard-Wilhelm
|e [editor]
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| 700 |
1 |
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|a Vasant, Pandian
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a EAI/Springer Innovations in Communication and Computing
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| 028 |
5 |
0 |
|a 10.1007/978-3-031-26496-2
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-031-26496-2?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 621,042
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| 520 |
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|a This book provides readers with emerging research that explores the theoretical and practical aspects of implementing new and innovative artificial intelligence (AI) techniques for renewable energy systems. The contributions offer broad coverage on economic and promotion policies of renewable energy and energy-efficiency technologies, the emerging fields of neuro-computational models and simulations under uncertainty (such as fuzzy-based computational models and fuzzy trace theory), evolutionary computation, metaheuristics, machine learning applications, advanced optimization, and stochastic models. This book is a pivotal reference for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in artificial intelligence, renewable energy systems, and energy autonomy. Based on sustainability as a fundamental factor for intelligent computing; Focuses on the role AI playsin smart living, energy transition, and sustainable development; Covers a broad range of green energy-related topics
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