TY - BOOK ID - 133567184 TI - Machine Learning for Energy Systems PY - 2020 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - History of engineering & technology KW - vacuum tank degasser KW - rule extraction KW - extreme learning machine KW - classification and regression trees KW - wind power: wind speed: T–S fuzzy model: forecasting KW - linearization KW - machine learning KW - photovoltaic output power forecasting KW - hybrid interval forecasting KW - relevance vector machine KW - sample entropy KW - ensemble empirical mode decomposition KW - high permeability renewable energy KW - blockchain technology KW - energy router KW - QoS index of energy flow KW - MOPSO algorithm KW - scheduling optimization KW - Adaptive Neuro-Fuzzy Inference System KW - insulator fault forecast KW - wavelet packets KW - time series forecasting KW - power quality KW - harmonic parameter KW - harmonic responsibility KW - monitoring data without phase angle KW - parameter estimation KW - blockchain KW - energy internet KW - information security KW - forecasting KW - clustering KW - energy systems KW - classification KW - integrated energy system KW - risk assessment KW - component accident set KW - vulnerability KW - hybrid AC/DC power system KW - stochastic optimization KW - renewable energy source KW - Volterra models KW - wind turbine KW - maintenance KW - fatigue KW - power control KW - offshore wind farm KW - Interfacial tension KW - transformer oil parameters KW - harmonic impedance KW - traction network KW - harmonic impedance identification KW - linear regression model KW - data evolution mechanism KW - cast-resin transformers KW - abnormal defects KW - partial discharge KW - pattern recognition KW - hierarchical clustering KW - decision tree KW - industrial mathematics KW - inverse problems KW - intelligent control KW - artificial intelligence KW - energy management system KW - smart microgrid KW - optimization KW - Volterra equations KW - energy storage KW - load leveling KW - cyber-physical systems UR - https://www.unicat.be/uniCat?func=search&query=sysid:133567184 AB - 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. ER -