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Energy Informatics: 5th Energy Informatics Academy Conference, EI.A 2025, Kuala Lumpur, Malaysia, December 35, 2025, Proceedings, Part I [Pehme köide]

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  • Formaat: Paperback / softback, 416 pages, kõrgus x laius: 235x155 mm, 115 Illustrations, color; 8 Illustrations, black and white
  • Sari: Lecture Notes in Computer Science
  • Ilmumisaeg: 17-Apr-2026
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3032191335
  • ISBN-13: 9783032191335
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Energy Informatics: 5th Energy Informatics Academy Conference, EI.A 2025, Kuala Lumpur, Malaysia, December 35, 2025, Proceedings, Part ISuurem pilt
  • Formaat: Paperback / softback, 416 pages, kõrgus x laius: 235x155 mm, 115 Illustrations, color; 8 Illustrations, black and white
  • Sari: Lecture Notes in Computer Science
  • Ilmumisaeg: 17-Apr-2026
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3032191335
  • ISBN-13: 9783032191335
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Teised raamatud sellest sooduspakkumisest: Kevadine sooduspakkumine - kuni 31. maini üle miljoni raamatu kuni 25% soodsamalt
Püsilink: https://www.kriso.ee/db/9783032191335.html
The two-volume set LNCS 16393 + 16394 constitutes the proceedings of the 5th Energy Informatics Academy Conference, EI.A 2025, held in Kuala Lumpur, Malaysia, during December 2025.



The 39 full papers and 14 short papers included in these volumes were carefully reviewed and selected from 115 submissions. They are organized in the following topical sections: 





Part I: Building Energy Forecasting & Modeling; Digital Infrastructure & Interoperability for Energy Systems; Local Energy Communities, Microgrids & Distributed Energy Resources; Data Centres, AI & Sustainable Energy Footprints; and  Context-Aware Digital Innovations for Energy Transitions.  



Part II: Cybersecurity & Communications in Digital Energy Systems; Energy Storage & Circular Resource Management; Methods, Governance & Responsible Digitalization in Energy; Thermal Storage and Flexibility in Buildings; and Digital Innovation for Resilient Low-Carbon Industry.

 
.- Building Energy Forecasting & Modeling.


.- Explainable Boosting Machine for Energy Consumption in Buildings.


.- Intelligent IoT Device Powered by AI-based Models for Forecast and
Classification.


.- Scalable estimation of energy savings via demand-temperature modeling in
commercial buildings.


.- Data-Driven Simplified RC-Based Thermal Modeling of Indoor Temperature
Dynamics.


.- CLASH: An energy-aware service for building load forecasting computation.