LEADER 04307nam 22005895 450 001 9910380729903321 005 20200630161126.0 010 $a981-15-2624-9 024 7 $a10.1007/978-981-15-2624-4 035 $a(CKB)4100000010474967 035 $a(DE-He213)978-981-15-2624-4 035 $a(MiAaPQ)EBC6121747 035 $a(PPN)242977928 035 $a(EXLCZ)994100000010474967 100 $a20200224d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSmart Meter Data Analytics $eElectricity Consumer Behavior Modeling, Aggregation, and Forecasting /$fby Yi Wang, Qixin Chen, Chongqing Kang 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (XXI, 293 p. 141 illus., 125 illus. in color.) 311 $a981-15-2623-0 327 $aOverview for Smart Meter Data Analytics -- Smart Meter Data Compression Based on Load Feature Identification -- A Combined Data-Driven Approach for Electricity Theft Detection -- GAN-based Model for Residential Load Generation -- Ensemble Clustering for Individual Electricity Consumption Patterns Extraction -- Sparse and Redundant Representation-Based Partial Usage Pattern Extraction -- Data-Driven Personalized Price Design in Retail Market Using Smart Meter Data -- Deep Learning-Based Socio-demographic Information Identification -- Cross-domain Feature Selection and Coding for Household Energy Behavior -- Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications -- Enhancing Short-term Probabilistic Residential Load Forecasting with Quantile LSTM -- An Ensemble Forecasting Method for the Aggregated Load With Subprofiles -- Prospects of Future Research Issues on Smart Meter Data Analytics. 330 $aThis book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems. 606 $aEnergy policy 606 $aEnergy and state 606 $aPower electronics 606 $aNatural resources 606 $aEnergy Policy, Economics and Management$3https://scigraph.springernature.com/ontologies/product-market-codes/112000 606 $aPower Electronics, Electrical Machines and Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24070 606 $aNatural Resource and Energy Economics$3https://scigraph.springernature.com/ontologies/product-market-codes/W48010 615 0$aEnergy policy. 615 0$aEnergy and state. 615 0$aPower electronics. 615 0$aNatural resources. 615 14$aEnergy Policy, Economics and Management. 615 24$aPower Electronics, Electrical Machines and Networks. 615 24$aNatural Resource and Energy Economics. 676 $a621.31 700 $aWang$b Yi$4aut$4http://id.loc.gov/vocabulary/relators/aut$0927188 702 $aChen$b Qixin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aKang$b Chongqing$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910380729903321 996 $aSmart Meter Data Analytics$92083270 997 $aUNINA