Torrent details for "Khooban M. Applications of Deep Machine Learning in Future Energ…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
12.2 MB
Info Hash:
8C70CE1B2E2009B52689433583217CF478545CDA
Added By:
Added:
April 22, 2026, 11:36 p.m.
Stats:
|
(Last updated: April 22, 2026, 11:40 p.m.)
| File | Size |
|---|---|
| Khooban M. Applications of Deep Machine Learning in Future Energy Systems 2024.pdf | 12.2 MB |
Name
DL
Uploader
Size
S/L
Added
NOTE
SOURCE: Khooban M. Applications of Deep Machine Learning in Future Energy Systems 2024
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format Applications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and operation before laying out current AI approaches and limitations. Later chapters provide in-depth accounts of specific challenges and the use of innovative third-generation machine learning, including neuromorphic computing, to resolve issues from security to power supply. An essential tool for the management, control, and modelling of future energy systems, this book maps a practical path towards AI capable of supporting sustainable energy. Clarifies the current state and future trends of energy system machine learning and the pitfalls facing our transitioning systems Provides guidance on 3rd-generation AI tools for meeting the challenges of modeling and control in modern energy systems Includes case studies and practical examples of potential applications to inspire and inform researchers and industry developers
×