TY - BOOK ID - 65157740 TI - Artificial Intelligence Techniques for a Scalable Energy Transition : Advanced Methods, Digital Technologies, Decision Support Tools, and Applications PY - 2020 SN - 3030427269 3030427250 9783030427269 PB - Cham : Springer, DB - UniCat KW - Artificial intelligence KW - Environmental applications. KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Electrical engineering. KW - Computational intelligence. KW - Artificial intelligence. KW - Data mining. KW - Big data. KW - Communications Engineering, Networks. KW - Computational Intelligence. KW - Artificial Intelligence. KW - Data Mining and Knowledge Discovery. KW - Big Data/Analytics. KW - Data sets, Large KW - Large data sets KW - Data sets KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - Intelligence, Computational KW - Soft computing KW - Electric engineering KW - Engineering KW - Power resources KW - Engineering applications. KW - Data processing. KW - Energy KW - Energy resources KW - Power supply KW - Natural resources KW - Energy harvesting KW - Energy industries KW - Data processing UR - https://www.unicat.be/uniCat?func=search&query=sysid:65157740 AB - 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.). 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. ER -