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- Leidėjas: Springer
- ISBN-10: 3030427285
- ISBN-13: 9783030427283
- Formatas: 15.6 x 23.4 x 2.1 cm, minkšti viršeliai
- Kalba: Anglų
- Extra -25 % nuolaida šiai knygai su kodu: ENG25
Artificial Intelligence Techniques for a Scalable Energy Transition (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
This 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.).
EXTRA 25 % nuolaida su kodu: ENG25
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- Leidėjas: Springer
- ISBN-10: 3030427285
- ISBN-13: 9783030427283
- Formatas: 15.6 x 23.4 x 2.1 cm, minkšti viršeliai
- Kalba: Anglų Anglų
This 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.).
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