Deep Learning for Power System Applications
Case Studies Linking Artificial Intelligence and Power Systems
Series: Power Electronics and Power Systems;
- Publisher's listprice EUR 117.69
-
49 924 Ft (47 546 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 8% (cc. 3 994 Ft off)
- Discounted price 45 929 Ft (43 742 Ft + 5% VAT)
49 924 Ft
Availability
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Springer
- Date of Publication 12 November 2024
- Number of Volumes 1 pieces, Book
- ISBN 9783031453595
- Binding Paperback
- No. of pages101 pages
- Size 235x155 mm
- Language English
- Illustrations 2 Illustrations, black & white; 30 Illustrations, color 670
Categories
Short description:
- 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.
Long description:
- 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.
Table of Contents:
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.
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