LEADER 04662nam 2201189z- 450 001 9910557678803321 005 20210501 035 $a(CKB)5400000000044745 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69401 035 $a(oapen)doab69401 035 $a(EXLCZ)995400000000044745 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning for Energy Systems 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (272 p.) 311 08$a3-03943-382-2 311 08$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 and technology$2bicssc 610 $aabnormal defects 610 $aAdaptive Neuro-Fuzzy Inference System 610 $aartificial intelligence 610 $ablockchain 610 $ablockchain technology 610 $acast-resin transformers 610 $aclassification 610 $aclassification and regression trees 610 $aclustering 610 $acomponent accident set 610 $acyber-physical systems 610 $adata evolution mechanism 610 $adecision tree 610 $aenergy internet 610 $aenergy management system 610 $aenergy router 610 $aenergy storage 610 $aenergy systems 610 $aensemble empirical mode decomposition 610 $aextreme learning machine 610 $afatigue 610 $aforecasting 610 $aharmonic impedance 610 $aharmonic impedance identification 610 $aharmonic parameter 610 $aharmonic responsibility 610 $ahierarchical clustering 610 $ahigh permeability renewable energy 610 $ahybrid AC/DC power system 610 $ahybrid interval forecasting 610 $aindustrial mathematics 610 $ainformation security 610 $ainsulator fault forecast 610 $aintegrated energy system 610 $aintelligent control 610 $aInterfacial tension 610 $ainverse problems 610 $alinear regression model 610 $alinearization 610 $aload leveling 610 $amachine learning 610 $amaintenance 610 $amonitoring data without phase angle 610 $aMOPSO algorithm 610 $aoffshore wind farm 610 $aoptimization 610 $aparameter estimation 610 $apartial discharge 610 $apattern recognition 610 $aphotovoltaic output power forecasting 610 $apower control 610 $apower quality 610 $aQoS index of energy flow 610 $arelevance vector machine 610 $arenewable energy source 610 $arisk assessment 610 $arule extraction 610 $asample entropy 610 $ascheduling optimization 610 $asmart microgrid 610 $astochastic optimization 610 $atime series forecasting 610 $atraction network 610 $atransformer oil parameters 610 $avacuum tank degasser 610 $aVolterra equations 610 $aVolterra models 610 $avulnerability 610 $awavelet packets 610 $awind power: wind speed: T-S fuzzy model: forecasting 610 $awind turbine 615 7$aHistory of engineering and 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