LEADER 04367nam 2201177z- 450 001 9910557645803321 005 20231214133627.0 035 $a(CKB)5400000000044995 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76890 035 $a(EXLCZ)995400000000044995 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEnergy Data Analytics for Smart Meter Data 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (346 p.) 311 $a3-0365-2016-3 311 $a3-0365-2017-1 330 $aThe principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal. 606 $aTechnology: general issues$2bicssc 610 $asmart grid 610 $anontechnical losses 610 $aelectricity theft detection 610 $asynthetic minority oversampling technique 610 $aK-means cluster 610 $arandom forest 610 $asmart grids 610 $asmart energy system 610 $asmart meter 610 $aGDPR 610 $adata privacy 610 $aethics 610 $amulti-label learning 610 $aNon-intrusive Load Monitoring 610 $aappliance recognition 610 $afryze power theory 610 $aV-I trajectory 610 $aConvolutional Neural Network 610 $adistance similarity matrix 610 $aactivation current 610 $aelectric vehicle 610 $asynthetic data 610 $aexponential distribution 610 $aPoisson distribution 610 $aGaussian mixture models 610 $amathematical modeling 610 $amachine learning 610 $asimulation 610 $aNon-Intrusive Load Monitoring (NILM) 610 $aNILM datasets 610 $apower signature 610 $aelectric load simulation 610 $adata-driven approaches 610 $asmart meters 610 $atext convolutional neural networks (TextCNN) 610 $atime-series classification 610 $adata annotation 610 $anon-intrusive load monitoring 610 $asemi-automatic labeling 610 $aappliance load signatures 610 $aambient influences 610 $adevice classification accuracy 610 $aNILM 610 $asignature 610 $aload disaggregation 610 $atransients 610 $apulse generator 610 $asmart metering 610 $asmart power grids 610 $apower consumption data 610 $aenergy data processing 610 $auser-centric applications of energy data 610 $aconvolutional neural network 610 $aenergy consumption 610 $aenergy data analytics 610 $aenergy disaggregation 610 $areal-time 610 $asmart meter data 610 $atransient load signature 610 $aattention mechanism 610 $adeep neural network 610 $aelectrical energy 610 $aload scheduling 610 $asatisfaction 610 $aShapley Value 610 $asolar photovoltaics 610 $areview 610 $adeep learning 610 $adeep neural networks 615 7$aTechnology: general issues 700 $aReinhardt$b Andreas$4edt$01295460 702 $aPereira$b Lucas$4edt 702 $aReinhardt$b Andreas$4oth 702 $aPereira$b Lucas$4oth 906 $aBOOK 912 $a9910557645803321 996 $aEnergy Data Analytics for Smart Meter Data$93023469 997 $aUNINA