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Machine Learning Applications for Intelligent Energy Management: Invited Chapters from Experts on the Energy Field [Pehme köide]

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  • Formaat: Paperback / softback, 226 pages, kõrgus x laius: 235x155 mm, 107 Illustrations, color; 3 Illustrations, black and white; XIV, 226 p. 110 illus., 107 illus. in color., 1 Paperback / softback
  • Sari: Learning and Analytics in Intelligent Systems 35
  • Ilmumisaeg: 14-Feb-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031479114
  • ISBN-13: 9783031479113
Teised raamatud teemal:
  • Pehme köide
  • Hind: 141,35 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 166,29 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Paperback / softback, 226 pages, kõrgus x laius: 235x155 mm, 107 Illustrations, color; 3 Illustrations, black and white; XIV, 226 p. 110 illus., 107 illus. in color., 1 Paperback / softback
  • Sari: Learning and Analytics in Intelligent Systems 35
  • Ilmumisaeg: 14-Feb-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031479114
  • ISBN-13: 9783031479113
Teised raamatud teemal:
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.
AI-Powered Transformation and Decentralization of the Energy
Ecosystem.- An Explainable AI-based Framework for Supporting Decisions in
Energy Management.- The big data value chain for the provision of AI-enabled
energy analytics services.- MODULAR BIG DATA APPLICATIONS FOR ENERGY SERVICES
IN BUILDINGS AND DISTRICTS: DIGITAL TWINS, TECHNICAL BUILDING MANAGEMENT
SYSTEMS AND ENERGY SAVINGS CALCULATIONS.- Neural network based approaches
for fault diagnosis of photovoltaic systems.- Clustering of building
stock.- BIG DATA SUPPORTED ANALYTICS FOR NEXT GENERATION ENERGY PERFORMANCE
CERTIFICATES.- Synthetic data on buildings.