Smart Energy and Electric Power Systems : Current Trends and New Intelligent Perspectives
Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-makin...
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| Diğer Yazarlar: | , , , |
| Materyal Türü: | Livre numérique |
| Dil: | Anglais |
| Baskı/Yayın Bilgisi: |
San Diego, CA :
Elsevier Science
2022.
Paris : Cyberlibris |
| Online Erişim: | Accès Université d'Orléans et IFPM Accès INSA CVL |
| Not: |
Couverture. https://static2.cyberlibris.com/books_upload/136pix/9780323916851.jpg Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Smart Energy and Electric Power Systems, Current Trends and New Intelligent Perspectives, Edited by Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen, Kayal Padmanandam, [et autres], San Diego, CA, Elsevier Science, 2022, 1 vol. (227 p.), 978-03-2391-664-6 |
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| 100 | 1 | |a Padmanaban, Sanjeevikumar. | |
| 245 | 1 | 0 | |a Smart Energy and Electric Power Systems : |b Current Trends and New Intelligent Perspectives |c Edited by Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen, Kayal Padmanandam, [et autres]. |
| 260 | |a San Diego, CA : |b Elsevier Science. | ||
| 260 | |a Paris : |b Cyberlibris, |c 2022. | ||
| 500 | |a Couverture. https://static2.cyberlibris.com/books_upload/136pix/9780323916851.jpg | ||
| 500 | |a Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur | ||
| 500 | |a Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur | ||
| 506 | |a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement. Cyberlibris | ||
| 520 | |a Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed include distributed energy resources and prosumer markets, electricity demand prediction, component fault detection, and load balancing. Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector. Helps improve the prediction capability of AI algorithms to make evidence-based decisions in the smart supply of electricity, including load shedding Focuses on how to integrate AI and ML into the energy sector in the real-world, with many chapters accompanied by case studies Addresses a number of proven AI and ML- informed techniques in rebalancing supply and demand | ||
| 700 | 1 | |a Holm-Nielsen, Jens Bo, |d 19..- |4 edt | |
| 700 | 1 | |a Padmanandam, Kayal. |4 edt | |
| 700 | 1 | |a Dhanaraj, Rajesh Kumar, |d 19..- |4 edt | |
| 700 | 1 | |a Balusamy, Balamurugan, |d 19..- |4 edt | |
| 776 | 0 | |t Smart Energy and Electric Power Systems |o Current Trends and New Intelligent Perspectives |f Edited by Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen, Kayal Padmanandam, [et autres] |c San Diego, CA |n Elsevier Science |d 2022 |p 1 vol. (227 p.) |z 978-03-2391-664-6 | |
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