LEADER 04643nam 2201177z- 450 001 9910557678803321 005 20231214133101.0 035 $a(CKB)5400000000044745 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69401 035 $a(EXLCZ)995400000000044745 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Energy Systems 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (272 p.) 311 $a3-03943-382-2 311 $a3-03943-383-0 330 $aThis 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. 606 $aHistory of engineering & technology$2bicssc 610 $avacuum tank degasser 610 $arule extraction 610 $aextreme learning machine 610 $aclassification and regression trees 610 $awind power: wind speed: T?S fuzzy model: forecasting 610 $alinearization 610 $amachine learning 610 $aphotovoltaic output power forecasting 610 $ahybrid interval forecasting 610 $arelevance vector machine 610 $asample entropy 610 $aensemble empirical mode decomposition 610 $ahigh permeability renewable energy 610 $ablockchain technology 610 $aenergy router 610 $aQoS index of energy flow 610 $aMOPSO algorithm 610 $ascheduling optimization 610 $aAdaptive Neuro-Fuzzy Inference System 610 $ainsulator fault forecast 610 $awavelet packets 610 $atime series forecasting 610 $apower quality 610 $aharmonic parameter 610 $aharmonic responsibility 610 $amonitoring data without phase angle 610 $aparameter estimation 610 $ablockchain 610 $aenergy internet 610 $ainformation security 610 $aforecasting 610 $aclustering 610 $aenergy systems 610 $aclassification 610 $aintegrated energy system 610 $arisk assessment 610 $acomponent accident set 610 $avulnerability 610 $ahybrid AC/DC power system 610 $astochastic optimization 610 $arenewable energy source 610 $aVolterra models 610 $awind turbine 610 $amaintenance 610 $afatigue 610 $apower control 610 $aoffshore wind farm 610 $aInterfacial tension 610 $atransformer oil parameters 610 $aharmonic impedance 610 $atraction network 610 $aharmonic impedance identification 610 $alinear regression model 610 $adata evolution mechanism 610 $acast-resin transformers 610 $aabnormal defects 610 $apartial discharge 610 $apattern recognition 610 $ahierarchical clustering 610 $adecision tree 610 $aindustrial mathematics 610 $ainverse problems 610 $aintelligent control 610 $aartificial intelligence 610 $aenergy management system 610 $asmart microgrid 610 $aoptimization 610 $aVolterra equations 610 $aenergy storage 610 $aload leveling 610 $acyber-physical systems 615 7$aHistory of engineering & technology 700 $aSidorov$b Denis N$4edt$01329477 702 $aSidorov$b Denis N$4oth 906 $aBOOK 912 $a9910557678803321 996 $aMachine Learning for Energy Systems$93039487 997 $aUNINA