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082 0 4 _a006.3
_223
245 1 0 _aMachine Learning Applications for Intelligent Energy Management
_h[electronic resource] :
_bInvited Chapters from Experts on the Energy Field /
_cedited by Haris Doukas, Vangelis Marinakis, Elissaios Sarmas.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIV, 226 p. 110 illus., 107 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLearning and Analytics in Intelligent Systems,
_x2662-3455 ;
_v35
505 0 _aAI-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.
520 _aAs 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.
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aElectrical engineering.
650 0 _aArtificial intelligence.
650 0 _aEnergy policy.
650 0 _aEnergy and state.
650 1 4 _aComputational Intelligence.
650 2 4 _aElectrical and Electronic Engineering.
650 2 4 _aArtificial Intelligence.
650 2 4 _aEnergy Policy, Economics and Management.
700 1 _aDoukas, Haris.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMarinakis, Vangelis.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSarmas, Elissaios.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031479083
776 0 8 _iPrinted edition:
_z9783031479106
776 0 8 _iPrinted edition:
_z9783031479113
830 0 _aLearning and Analytics in Intelligent Systems,
_x2662-3455 ;
_v35
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-47909-0
912 _aZDB-2-INR
912 _aZDB-2-SXIT
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