Smart meter data analytics : electricity consumer behavior modeling, aggregation, and forecasting / Yi Wang, Qixin Chen, Chongqing Kang.
Wang, Yi.; Chen, Qixin.; Kang, Chongqing.
2020
TK3105
Available Online

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Title Smart meter data analytics : electricity consumer behavior modeling, aggregation, and forecasting / Yi Wang, Qixin Chen, Chongqing Kang.
Author Wang, Yi.
ISBN 9789811526244 (electronic book)
9811526249
9811526230
9789811526237
Publication Details Singapore : Springer, [2020]
Language English
Description 1 online resource (306 pages)
Item Number 10.1007/978-981-15-2
Call Number TK3105
Dewey Decimal Classification 621.31
333.79
Summary This 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Chen, Qixin.
Kang, Chongqing.
Available in Other Form Smart Meter Data Analytics : Electricity Consumer Behavior Modeling, Aggregation, and Forecasting.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Overview 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.

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