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Core concepts and methods in load forecastingwith applications in distribution networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Core concepts and methods in load forecastingby Stephen Haben, Marcus Voss, William Holderbaum.
Reminder of title:	with applications in distribution networks /
Author:	Haben, Stephen.
other author:	Voss, Marcus.
Published:	Cham :Springer International Publishing :2023.
Description:	xv, 331 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Electric power-plantsLoad
Online resource:	https://doi.org/10.1007/978-3-031-27852-5
ISBN:	9783031278525$q(electronic bk.)

Core concepts and methods in load forecastingwith applications in distribution networks /
Haben, Stephen.

Core concepts and methods in load forecastingwith applications in distribution networks /[electronic resource] :by Stephen Haben, Marcus Voss, William Holderbaum. - Cham :Springer International Publishing :2023. - xv, 331 p. :ill. (some col.), digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Primer on Distribution Electricity Networks -- Chapter 3. Primer on Statistics and Probability -- Chapter 4. Primer on Machine Learning -- Chapter 5. Time Series Forecasting: Core Concepts and Definitions -- Chapter 6. Load Data: Preparation, Analysis and Feature Generation -- Chapter 7. Verification and Evaluation of Load Forecast Models -- Chapter 8. Load Forecasting Model Training and Selection -- Chapter 9. Benchmark and Statistical Point Forecast Methods -- Chapter 10. Machine Learning Point Forecasts Methods -- Chapter 11. Probabilistic Forecast Methods -- Chapter 12. Load Forecast Process -- Chapter 13. Advanced and Additional Topics -- Chapter 14. Case Study: Low Voltage Demand Forecasts -- Chapter 15. Selected Applications and Examples -- Appendix.

Open access.

This comprehensive open access book enables readers to discover the essential techniques for load forecasting in electricity networks, particularly for active distribution networks. From statistical methods to deep learning and probabilistic approaches, the book covers a wide range of techniques and includes real-world applications and a worked examples using actual electricity data (including an example implemented through shared code) Advanced topics for further research are also included, as well as a detailed appendix on where to find data and additional reading. As the smart grid and low carbon economy continue to evolve, the proper development of forecasting methods is vital. This book is a must-read for students, industry professionals, and anyone interested in forecasting for smart control applications, demand-side response, energy markets, and renewable utilization.

ISBN: 9783031278525$q(electronic bk.)

Standard No.: 10.1007/978-3-031-27852-5doiSubjects--Topical Terms:

866249
Electric power-plants
--Load

LC Class. No.: TK1005 / .H33 2023

Dewey Class. No.: 621.3121

Core concepts and methods in load forecastingwith applications in distribution networks /
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245 1 0 $a Core concepts and methods in load forecasting $h [electronic resource] : $b with applications in distribution networks / $c by Stephen Haben, Marcus Voss, William Holderbaum.
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505 0 $a Chapter 1. Introduction -- Chapter 2. Primer on Distribution Electricity Networks -- Chapter 3. Primer on Statistics and Probability -- Chapter 4. Primer on Machine Learning -- Chapter 5. Time Series Forecasting: Core Concepts and Definitions -- Chapter 6. Load Data: Preparation, Analysis and Feature Generation -- Chapter 7. Verification and Evaluation of Load Forecast Models -- Chapter 8. Load Forecasting Model Training and Selection -- Chapter 9. Benchmark and Statistical Point Forecast Methods -- Chapter 10. Machine Learning Point Forecasts Methods -- Chapter 11. Probabilistic Forecast Methods -- Chapter 12. Load Forecast Process -- Chapter 13. Advanced and Additional Topics -- Chapter 14. Case Study: Low Voltage Demand Forecasts -- Chapter 15. Selected Applications and Examples -- Appendix.
506 $a Open access.
520 $a This comprehensive open access book enables readers to discover the essential techniques for load forecasting in electricity networks, particularly for active distribution networks. From statistical methods to deep learning and probabilistic approaches, the book covers a wide range of techniques and includes real-world applications and a worked examples using actual electricity data (including an example implemented through shared code) Advanced topics for further research are also included, as well as a detailed appendix on where to find data and additional reading. As the smart grid and low carbon economy continue to evolve, the proper development of forecasting methods is vital. This book is a must-read for students, industry professionals, and anyone interested in forecasting for smart control applications, demand-side response, energy markets, and renewable utilization.
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650 0 $a Electric power transmission $x Forecasting. $3 950438
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650 2 4 $a Electrical Power Engineering. $3 919410
650 2 4 $a Neural Circuits. $3 921029
650 2 4 $a Mechanical and Thermal Energy Storage. $3 918925
650 2 4 $a Control, Robotics, Automation. $3 914981
700 1 $a Voss, Marcus. $3 950436
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