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000929661 019__ $$a1142310029$$a1142527473$$a1142797083
000929661 020__ $$a9789811526244$$q(electronic book)
000929661 020__ $$a9811526249
000929661 020__ $$z9811526230
000929661 020__ $$z9789811526237
000929661 0248_ $$a10.1007/978-981-15-2
000929661 035__ $$aSP(OCoLC)on1142509782
000929661 035__ $$aSP(OCoLC)1142509782$$z(OCoLC)1142310029$$z(OCoLC)1142527473$$z(OCoLC)1142797083
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000929661 050_4 $$aQC71.82-73.8
000929661 08204 $$a621.31$$223
000929661 08204 $$a333.79
000929661 1001_ $$aWang, Yi.
000929661 24510 $$aSmart meter data analytics :$$belectricity consumer behavior modeling, aggregation, and forecasting /$$cYi Wang, Qixin Chen, Chongqing Kang.
000929661 260__ $$aSingapore :$$bSpringer,$$c[2020]
000929661 300__ $$a1 online resource (306 pages)
000929661 336__ $$atext$$btxt$$2rdacontent
000929661 337__ $$acomputer$$bc$$2rdamedia
000929661 338__ $$aonline resource$$bcr$$2rdacarrier
000929661 504__ $$aIncludes bibliographical references.
000929661 5050_ $$aOverview for Smart Meter Data Analytics -- Smart Meter Data Compression Based on Load Feature Identification -- A Combined Data-Driven Approach for Electricity Theft Detection -- GAN-based Model for Residential Load Generation -- Ensemble Clustering for Individual Electricity Consumption Patterns Extraction -- Sparse and Redundant Representation-Based Partial Usage Pattern Extraction -- Data-Driven Personalized Price Design in Retail Market Using Smart Meter Data -- Deep Learning-Based Socio-demographic Information Identification -- Cross-domain Feature Selection and Coding for Household Energy Behavior -- Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications -- Enhancing Short-term Probabilistic Residential Load Forecasting with Quantile LSTM -- An Ensemble Forecasting Method for the Aggregated Load With Subprofiles -- Prospects of Future Research Issues on Smart Meter Data Analytics.
000929661 506__ $$aAccess limited to authorized users.
000929661 520__ $$aThis book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.
000929661 588__ $$aDescription based on print version record.
000929661 650_0 $$aSmart power grids.
000929661 650_0 $$aElectric power consumption.
000929661 650_0 $$aElectric meters.
000929661 7001_ $$aChen, Qixin.
000929661 7001_ $$aKang, Chongqing.
000929661 77608 $$iPrint version:$$aWang, Yi.$$tSmart Meter Data Analytics : Electricity Consumer Behavior Modeling, Aggregation, and Forecasting.$$dSingapore : Springer, ©2020$$z9789811526237
000929661 852__ $$bebk
000929661 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-2624-4$$zOnline Access$$91397441.1
000929661 909CO $$ooai:library.usi.edu:929661$$pGLOBAL_SET
000929661 980__ $$aEBOOK
000929661 980__ $$aBIB
000929661 982__ $$aEbook
000929661 983__ $$aOnline
000929661 994__ $$a92$$bISE