Deep Learning for power system applications: case studies linking Artificial Intelligence and power systems
This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) fo...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2024]
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| Schriftenreihe: | Power electronics and power systems
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| Schlagworte: | |
| Zusammenfassung: | This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control.Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers.- Provides a history of AI in power grid operation and planning;- Introduces deep learning algorithms and applications in power systems;- Includes several representative case studies |
| Beschreibung: | Introduction-A Brief History of Deep Learning and Its Applications in Power Systems.- Deep Neural Network for Microgrid Management.- Deep Convolutional Neural Network for Power System N-1 Contingency Screening and Cascading Outage Screening.- Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning.- Summary and Future Works |
| Beschreibung: | xii, 101 Seiten Illustrationen 235 mm |
| ISBN: | 9783031453595 |
Internformat
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| 100 | 1 | |a Li, Fangxing |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Deep Learning for power system applications |b case studies linking Artificial Intelligence and power systems |c Fangxing Li, Yan Du |
| 264 | 1 | |a Cham, Switzerland |b Springer |c [2024] | |
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| 490 | 0 | |a Power electronics and power systems | |
| 500 | |a Introduction-A Brief History of Deep Learning and Its Applications in Power Systems.- Deep Neural Network for Microgrid Management.- Deep Convolutional Neural Network for Power System N-1 Contingency Screening and Cascading Outage Screening.- Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning.- Summary and Future Works | ||
| 520 | |a This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control.Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers.- Provides a history of AI in power grid operation and planning;- Introduces deep learning algorithms and applications in power systems;- Includes several representative case studies | ||
| 650 | 4 | |a Electric power distribution | |
| 650 | 4 | |a Energy policy | |
| 650 | 4 | |a Energy and state | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Electric power production | |
| 653 | |a Hardcover, Softcover / Technik/Elektronik, Elektrotechnik, Nachrichtentechnik | ||
| 700 | 1 | |a Du, Yan |e Verfasser |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-031-45357-1 |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035474049 | |
Datensatz im Suchindex
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| bvnumber | BV050137517 |
| ctrlnum | (OCoLC)1470979859 (DE-599)BVBBV050137517 |
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| id | DE-604.BV050137517 |
| illustrated | Illustrated |
| indexdate | 2025-03-31T18:08:41Z |
| institution | BVB |
| isbn | 9783031453595 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035474049 |
| oclc_num | 1470979859 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | xii, 101 Seiten Illustrationen 235 mm |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| record_format | marc |
| series2 | Power electronics and power systems |
| spelling | Li, Fangxing Verfasser aut Deep Learning for power system applications case studies linking Artificial Intelligence and power systems Fangxing Li, Yan Du Cham, Switzerland Springer [2024] xii, 101 Seiten Illustrationen 235 mm txt rdacontent n rdamedia nc rdacarrier Power electronics and power systems Introduction-A Brief History of Deep Learning and Its Applications in Power Systems.- Deep Neural Network for Microgrid Management.- Deep Convolutional Neural Network for Power System N-1 Contingency Screening and Cascading Outage Screening.- Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning.- Summary and Future Works This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control.Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers.- Provides a history of AI in power grid operation and planning;- Introduces deep learning algorithms and applications in power systems;- Includes several representative case studies Electric power distribution Energy policy Energy and state Artificial intelligence Machine learning Electric power production Hardcover, Softcover / Technik/Elektronik, Elektrotechnik, Nachrichtentechnik Du, Yan Verfasser aut Erscheint auch als Online-Ausgabe 978-3-031-45357-1 |
| spellingShingle | Li, Fangxing Du, Yan Deep Learning for power system applications case studies linking Artificial Intelligence and power systems Electric power distribution Energy policy Energy and state Artificial intelligence Machine learning Electric power production |
| title | Deep Learning for power system applications case studies linking Artificial Intelligence and power systems |
| title_auth | Deep Learning for power system applications case studies linking Artificial Intelligence and power systems |
| title_exact_search | Deep Learning for power system applications case studies linking Artificial Intelligence and power systems |
| title_full | Deep Learning for power system applications case studies linking Artificial Intelligence and power systems Fangxing Li, Yan Du |
| title_fullStr | Deep Learning for power system applications case studies linking Artificial Intelligence and power systems Fangxing Li, Yan Du |
| title_full_unstemmed | Deep Learning for power system applications case studies linking Artificial Intelligence and power systems Fangxing Li, Yan Du |
| title_short | Deep Learning for power system applications |
| title_sort | deep learning for power system applications case studies linking artificial intelligence and power systems |
| title_sub | case studies linking Artificial Intelligence and power systems |
| topic | Electric power distribution Energy policy Energy and state Artificial intelligence Machine learning Electric power production |
| topic_facet | Electric power distribution Energy policy Energy and state Artificial intelligence Machine learning Electric power production |
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