LEADER 02375nam 2200505 450 001 9910819095003321 005 20231110222545.0 010 $a2-8062-2578-7 035 $a(CKB)3710000000306867 035 $a(EBL)1869929 035 $a(MiAaPQ)EBC1869929 035 $a(Au-PeEL)EBL1869929 035 $a(OCoLC)897070919 035 $a(PPN)188690689 035 $a(EXLCZ)993710000000306867 100 $a20220519d2011 uy 1 101 0 $afre 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aLa Belle et la Bete de Madame Leprince de Beaumont (Analyse de L'oeuvre) $eAnalyse Complete et Resume detaille de L'oeuvre /$fEliane Choffray, Margot Pepin 210 1$aCork, Ireland :$cLemaitre Publishing,$d[2011] 210 4$dİ2011 215 $a1 online resource (23 p.) 225 1 $aFiche de Lecture 300 $aDescription based upon print version of record. 311 $a2-8062-2580-9 327 $a1. Re?sume?; 2. E?tude des personnages; Belle; Les deux s?urs; Le marchand; La Be?te; 3. Cle?s de lecture; Sche?ma actanciel; Sche?ma narratif; Le conte merveilleux; 4. Informations comple?mentaires 330 $aTout ce qu'il faut savoir sur La Belle et la Be?te de Mme Leprince de Beaumont ! Retrouvez l'essentiel de l'?uvre dans une fiche de lecture comple?te et de?taille?e, avec un re?sume?, une e?tude des personnages, des sche?mas actanciel et narratif, et des cle?s de lecture. Re?dige?e de manie?re claire et accessible, la fiche de lecture propose d'abord un re?sume? inte?gral du conte, puis s'inte?resse aux diffe?rents personnages : la charmante Belle, ses deux me?chantes s?urs, le marchand et la Be?te. Apre?s les sche?mas actanciel et narratif, on aborde le genre du conte merveilleux. Une analyse litte?raire de re?f 410 0$aFiche de Lecture 606 $aMonsters$vFiction 606 $aPride and vanity$vFiction 615 0$aMonsters 615 0$aPride and vanity 676 $a001.944 700 $aChoffray$b Eliane$0939742 702 $aPepin$b Margot 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910819095003321 996 $aLa Belle et la Bete de Madame Leprince de Beaumont (Analyse de L'oeuvre)$93971648 997 $aUNINA LEADER 04336nam 22006015 450 001 9910380729903321 005 20250609111905.0 010 $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(MiAaPQ)EBC6122065 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 $a1 online resource (XXI, 293 p. 141 illus., 125 illus. in color.) 311 08$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 policy 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$aEnergy policy. 615 0$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