Machine Learning Applications for Intelligent Energy Management: Invited Chapters from Experts on the Energy Field
Language: English
Published by Springer Nature, 2024
- Hardcover
- New

Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
AbeBooks seller since January 6, 2003
Condition: New
US$ 289.53
Quantity: 2 available
Add to basketItem description from seller
240 pages. 9.26x6.10x0.63 inches. In Stock.
Seller Inventory # x-3031479084
- Title
- Machine Learning Applications for Intelligent Energy Management: Invited Chapters from Experts on the Energy Field
- Author
- Doukas, Haris (Editor)/ Marinakis, Vangelis (Editor)/ Sarmas, Elissaios (Editor)
- Publisher
- Springer Nature
- Publication year
- 2024
- Condition
- Brand New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3031479084
- ISBN 13
- 9783031479083
- Item weight
- 0.61 kilograms
As carbon dioxide (CO2) emissions and other greenhouse gases constantly rise and constitute the main contributor to climate change, temperature rise and global warming, artificial intelligence, big data, Internet of things, and blockchain technologies are enlisted to help enforce energy transition and transform the entire energy sector.
The book at hand presents state-of-the-art developments in artificial intelligence-empowered analytics of energy data and artificial intelligence-empowered application development. Topics covered include a presentation of the various stakeholders in the energy sector and their corresponding required analytic services, such as state-of-the-art machine learning, artificial intelligence, and optimization models and algorithms tailored for a series of demanding energy problems and aiming at providing optimal solutions under specific constraints.
Professors, researchers, scientists, engineers, and students in energy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.
"Synopsis" may belong to another edition of this title.
From the Back Cover
As carbon dioxide (CO2) emissions and other greenhouse gases constantly rise and constitute the main contributor to climate change, temperature rise and global warming, artificial intelligence, big data, Internet of things, and blockchain technologies are enlisted to help enforce energy transition and transform the entire energy sector.
The book at hand presents state-of-the-art developments in artificial intelligence-empowered analytics of energy data and artificial intelligence-empowered application development. Topics covered include a presentation of the various stakeholders in the energy sector and their corresponding required analytic services, such as state-of-the-art machine learning, artificial intelligence, and optimization models and algorithms tailored for a series of demanding energy problems and aiming at providing optimal solutions under specific constraints.
Professors, researchers, scientists, engineers, and students inenergy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.
"About the title" may belong to another edition of this title.
Revaluation Books
Exeter, United Kingdom
AbeBooks seller since January 6, 2003
Shipping rates from United Kingdom to U.S.A.
| Item | 7 to 14 business days | 2 to 3 business days |
|---|---|---|
| First item | US$ 16.74 | US$ 33.49 |
Payment methods
Seller's business information
Edward Bowditch Ltd
Exstowe, Exton
Exeter, United Kingdom EX3 0PP
Terms of sale
Legal entity name: Edward Bowditch Ltd
Legal entity form: Limited company
Business correspondence address: Exstowe, Exton, Exeter, EX3 0PP
Company registration number: 04916632
VAT registration: GB834241546
Authorised representative: Mr. E. Bowditch
Shipping terms
Orders usually dispatched within two working days. Please note that at this time all domestic United Kingdom orders are sent by trackable UPS courier, we choose not to offer a lower cost alternative.