TY  - GEN
N2  - 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.
AB  - 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.
T1  - Smart meter data analytics :electricity consumer behavior modeling, aggregation, and forecasting /
DA  - [2020]
CY  - Singapore :
AU  - Wang, Yi.
AU  - Chen, Qixin.
AU  - Kang, Chongqing.
CN  - TK3105
CN  - QC71.82-73.8
PB  - Springer,
PP  - Singapore :
PY  - [2020]
ID  - 929661
KW  - Smart power grids.
KW  - Electric power consumption.
KW  - Electric meters.
SN  - 9789811526244
SN  - 9811526249
TI  - Smart meter data analytics :electricity consumer behavior modeling, aggregation, and forecasting /
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-2624-4
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-2624-4
ER  -