Machine Learning for Energy Systems

• Autore: Sidorov Denis N
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (272 p.)
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: abnormal defects; Adaptive Neuro-Fuzzy Inference System; artificial intelligence; blockchain; blockchain technology; cast-resin transformers; classification; classification and regression trees; clustering; component accident set; cyber-physical systems; data evolution mechanism; decision tree; energy internet; energy management system; energy router; energy storage; energy systems; ensemble empirical mode decomposition; extreme learning machine; fatigue; forecasting; harmonic impedance; harmonic impedance identification; harmonic parameter; harmonic responsibility; hierarchical clustering; high permeability renewable energy; hybrid AC/DC power system; hybrid interval forecasting; industrial mathematics; information security; insulator fault forecast; integrated energy system; intelligent control; Interfacial tension; inverse problems; linear regression model; linearization; load leveling; machine learning; maintenance; monitoring data without phase angle; MOPSO algorithm; offshore wind farm; optimization; parameter estimation; partial discharge; pattern recognition; photovoltaic output power forecasting; power control; power quality; QoS index of energy flow; relevance vector machine; renewable energy source; risk assessment; rule extraction; sample entropy; scheduling optimization; smart microgrid; stochastic optimization; time series forecasting; traction network; transformer oil parameters; vacuum tank degasser; Volterra equations; Volterra models; vulnerability; wavelet packets; wind power: wind speed: T-S fuzzy model: forecasting; wind turbine
• Persona (resp. second.): SidorovDenis N
• Sommario/riassunto: This volume deals with recent advances in and applications of computational intelligence and advanced machine learning methods in power systems, heating and cooling systems, and gas transportation systems. The optimal coordinated dispatch of the multi-energy microgrids with renewable generation and storage control using advanced numerical methods is discussed. Forecasting models are designed for electrical insulator faults, the health of the battery, electrical insulator faults, wind speed and power, PV output power and transformer oil test parameters. The loads balance algorithm for an offshore wind farm is proposed. The information security problems in the energy internet are analyzed and attacked using information transmission contemporary models, based on blockchain technology. This book will be of interest, not only to electrical engineers, but also to applied mathematicians who are looking for novel challenging problems to focus on.
• Titolo autorizzato: Machine Learning for Energy Systems
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557678803321
• Lo trovi qui: Univ. Federico II