Artificial Intelligence for Renewable Energy systems
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Form:Einzelkauf Download
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Sprache:Englisch
235,00 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
01.08.2022
Herausgeber
Ashutosh Kumar Dubey + weitereVerlag
Elsevier Science & Techn.Seitenzahl
406 (Printausgabe)
Dateigröße
29314 KB
Sprache
Englisch
EAN
9780323906616
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.
- 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
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
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