Springer Smart Meter Data Analytics: Electricity Consumer Behavior Modeling, Aggregation, and Forecasting

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. From the Back Cover 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. About the Author Yi Wang is a Postdoctoral Researcher at ETH Zurich. He received his Bachelor's degree in electrical engineering from Huazhong University of Science and Technology (HUST) in 2014 and a Ph.D. degree in electrical engineering at Tsinghua University in 2019. From 2017 to 2018, he was an exchange student researcher at University of Washington. His research interests include big data applications in the smart grid, multiple energy systems, and cyber-physical power distribution systems. He currently serves as the secretary of IEEE PES Working Group on Energy Forecasting and Analytics. He is the reviewer of over 20 journals and has been awarded as best reviewer of IEEE Transactions several times. He also serves as the associate editor for IET Renewable Power Generation, IET Smart Grid, and International Transactions on Electrical Energy Systems. He was awarded as excellent graduate student of Tsinghua University and Siebel Scholar. Qixin Chen is an Associate Professor at Tsinghua University. He is also an Associate Director for Energy Internet Research Institute, Tsinghua University. He received both the B.S. and Ph.D. degrees from the Department of Electrical Engineering, Tsinghua University in 2005 and 2010, respectively. He had ever worked as a Research Assistant in School of Electrical and Electronic Engineering at the University of Manchester, United Kingdom in 2008. His research interests include power market, low carbon electricity technology, data analytics for smart grid. He is an IEEE Senior Member. He is the Chair of IEEE Working Group on Load Aggregation and Distribution Market. He was granted the National Science Fund for Disting