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 commodity to market-based technological advance...
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, MA
Woodhead Publishing, Elsevier
[2022]
|
| Schriftenreihe: | Woodhead Publishing series in energy
|
| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | 1. Current State of energy systems; 2. 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 |
| Beschreibung: | xvi, 389 Seiten Illustrationen, Diagramme 229 mm |
| ISBN: | 9780323903967 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV048358212 | ||
| 003 | DE-604 | ||
| 005 | 20231221 | ||
| 007 | t | ||
| 008 | 220713s2022 a||| |||| 00||| eng d | ||
| 020 | |a 9780323903967 |c pbk |9 978-0-323-90396-7 | ||
| 024 | 3 | |a 9780323903967 | |
| 035 | |a (OCoLC)1346090480 | ||
| 035 | |a (DE-599)BVBBV048358212 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-29T |a DE-898 | ||
| 084 | |a ZP 3700 |0 (DE-625)157967: |2 rvk | ||
| 245 | 1 | 0 | |a Artificial intelligence for renewable energy systems |c edited by Ashutosh Kumar Dubey, Sushil Kumar Narang, Arun Lal Srivastav, Abhishek Kumar, Vicente García-Díaz |
| 264 | 1 | |a Cambridge, MA |b Woodhead Publishing, Elsevier |c [2022] | |
| 300 | |a xvi, 389 Seiten |b Illustrationen, Diagramme |c 229 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Woodhead Publishing series in energy | |
| 500 | |a 1. Current State of energy systems; 2. 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 | ||
| 520 | |a 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 | ||
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 700 | 1 | |a Dubey, Ashutosh Kumar |4 edt | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033737448 | ||
Datensatz im Suchindex
| _version_ | 1804184219164868608 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author2 | Dubey, Ashutosh Kumar |
| author2_role | edt |
| author2_variant | a k d ak akd |
| author_facet | Dubey, Ashutosh Kumar |
| building | Verbundindex |
| bvnumber | BV048358212 |
| classification_rvk | ZP 3700 |
| ctrlnum | (OCoLC)1346090480 (DE-599)BVBBV048358212 |
| discipline | Energietechnik |
| discipline_str_mv | Energietechnik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03396nam a2200325 c 4500</leader><controlfield tag="001">BV048358212</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20231221 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">220713s2022 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780323903967</subfield><subfield code="c">pbk</subfield><subfield code="9">978-0-323-90396-7</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9780323903967</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1346090480</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048358212</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield><subfield code="a">DE-898</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ZP 3700</subfield><subfield code="0">(DE-625)157967:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Artificial intelligence for renewable energy systems</subfield><subfield code="c">edited by Ashutosh Kumar Dubey, Sushil Kumar Narang, Arun Lal Srivastav, Abhishek Kumar, Vicente García-Díaz</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge, MA</subfield><subfield code="b">Woodhead Publishing, Elsevier</subfield><subfield code="c">[2022]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xvi, 389 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">229 mm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Woodhead Publishing series in energy</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">1. Current State of energy systems; 2. 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</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dubey, Ashutosh Kumar</subfield><subfield code="4">edt</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033737448</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV048358212 |
| illustrated | Illustrated |
| index_date | 2024-07-03T20:14:01Z |
| indexdate | 2024-07-10T09:35:51Z |
| institution | BVB |
| isbn | 9780323903967 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033737448 |
| oclc_num | 1346090480 |
| open_access_boolean | |
| owner | DE-29T DE-898 DE-BY-UBR |
| owner_facet | DE-29T DE-898 DE-BY-UBR |
| physical | xvi, 389 Seiten Illustrationen, Diagramme 229 mm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Woodhead Publishing, Elsevier |
| record_format | marc |
| series2 | Woodhead Publishing series in energy |
| spelling | Artificial intelligence for renewable energy systems edited by Ashutosh Kumar Dubey, Sushil Kumar Narang, Arun Lal Srivastav, Abhishek Kumar, Vicente García-Díaz Cambridge, MA Woodhead Publishing, Elsevier [2022] xvi, 389 Seiten Illustrationen, Diagramme 229 mm txt rdacontent n rdamedia nc rdacarrier Woodhead Publishing series in energy 1. Current State of energy systems; 2. 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 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 (DE-588)4143413-4 Aufsatzsammlung gnd-content Dubey, Ashutosh Kumar edt |
| spellingShingle | Artificial intelligence for renewable energy systems |
| subject_GND | (DE-588)4143413-4 |
| title | Artificial intelligence for renewable energy systems |
| title_auth | Artificial intelligence for renewable energy systems |
| title_exact_search | Artificial intelligence for renewable energy systems |
| title_exact_search_txtP | Artificial intelligence for renewable energy systems |
| title_full | Artificial intelligence for renewable energy systems edited by Ashutosh Kumar Dubey, Sushil Kumar Narang, Arun Lal Srivastav, Abhishek Kumar, Vicente García-Díaz |
| title_fullStr | Artificial intelligence for renewable energy systems edited by Ashutosh Kumar Dubey, Sushil Kumar Narang, Arun Lal Srivastav, Abhishek Kumar, Vicente García-Díaz |
| title_full_unstemmed | Artificial intelligence for renewable energy systems edited by Ashutosh Kumar Dubey, Sushil Kumar Narang, Arun Lal Srivastav, Abhishek Kumar, Vicente García-Díaz |
| title_short | Artificial intelligence for renewable energy systems |
| title_sort | artificial intelligence for renewable energy systems |
| topic_facet | Aufsatzsammlung |
| work_keys_str_mv | AT dubeyashutoshkumar artificialintelligenceforrenewableenergysystems |