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020 _a9783030427269
024 7 _a10.1007/978-3-030-42726-9
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQ335
_b2020 EB
245 0 0 _aArtificial Intelligence Techniques for a Scalable Energy Transition
_bAdvanced Methods, Digital Technologies, Decision Support Tools, and Applications
_cedited by Moamar Sayed-Mouchaweh
250 _aFirst edition
264 1 _aCham
_bSpringer International Publishing :
_bImprint: Springer
_c2020
300 _a1 recurso en línea (IX, 382 páginas)
_b146 ilustraciones, 120 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _aArchivo de texto
_bPDF
490 0 _aEngineering (Springer-11647)
505 0 _aIntroduction -- Definition, motivation and impact of digitalization in energy transition -- Definition of a general scheme (layers) of a digitalized system in energy transition -- Challenges of digitalization in energy transition -- Artificial Intelligence for energy transition -- General principals and classification of Artificial Intelligence techniques for energy transition -- Artificial Intelligence for Smart Energy Management -- Smart energy management (intrusive and non-intrusive load monitoring) -- Artificial Intelligence for Cyber Security and Privacy -- Artificial Intelligence for Mobility and Electrical Vehicles -- Mobility and electrical vehicles -- Artificial Intelligence for Micro Grid Operations and Dynamic Pricing Revenue Management -- Micro Grid operations and Dynamic Pricing Revenue Management -- Artificial Intelligence for Renewable Energy Penetration and Demand Side Management -- Renewable Energy Penetration and Demand Side Management -- Emerging Trends, Open problems, and Future Challenges -- Conclusion.
520 3 _aThis book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.). Uses examples and applications to facilitate the understanding of AI techniques for scalable energy transitions Includes examples, problems, and techniques in order to increase transparency and understanding of the methodological concepts Dedicated to researchers, practitioners, and operators working with industrial systems.
988 _aSpringer_Engineering_23062020
650 7 _2embne
_aInteligencia artificial
_9413115
650 7 _2embne
_aEnergía eléctrica
_xProducción
_9168909
700 1 _aSayed-Mouchaweh, Moamar
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_0http://id.loc.gov/authorities/names/no2012100949
_1http://viaf.org/viaf/257736158
710 2 _aSpringerLink (Online service)
_0http://id.loc.gov/authorities/names/no2005046756
_1http://viaf.org/viaf/148105729
776 0 8 _iPrinted edition:
_z9783030427252
776 0 8 _iPrinted edition:
_z9783030427276
776 0 8 _iPrinted edition:
_z9783030427283
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-42726-9
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b07/2020
_dz
_eIG
_zSI