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Smart Meter Data Analytics - Electricity Consumer Behavior Modeling, Aggregation, and Forecasting (Hardcover, 1st ed. 2020): Yi... Smart Meter Data Analytics - Electricity Consumer Behavior Modeling, Aggregation, and Forecasting (Hardcover, 1st ed. 2020)
Yi Wang, Qixin Chen, Chongqing Kang
R3,631 Discovery Miles 36 310 Ships in 12 - 17 working days

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.

Data Analytics in Power Markets (Hardcover, 1st ed. 2021): Qixin Chen, Hongye Guo, Kedi Zheng, Yi Wang Data Analytics in Power Markets (Hardcover, 1st ed. 2021)
Qixin Chen, Hongye Guo, Kedi Zheng, Yi Wang
R3,630 Discovery Miles 36 300 Ships in 12 - 17 working days

This book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.

Data Analytics in Power Markets (Paperback, 1st ed. 2021): Qixin Chen, Hongye Guo, Kedi Zheng, Yi Wang Data Analytics in Power Markets (Paperback, 1st ed. 2021)
Qixin Chen, Hongye Guo, Kedi Zheng, Yi Wang
R3,992 Discovery Miles 39 920 Ships in 10 - 15 working days

This book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.

Smart Meter Data Analytics - Electricity Consumer Behavior Modeling, Aggregation, and Forecasting (Paperback, 1st ed. 2020): Yi... Smart Meter Data Analytics - Electricity Consumer Behavior Modeling, Aggregation, and Forecasting (Paperback, 1st ed. 2020)
Yi Wang, Qixin Chen, Chongqing Kang
R2,795 Discovery Miles 27 950 Ships in 10 - 15 working days

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.

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