東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Core concepts and methods in load fo...

Haben, Stephen.

Linked to FindBook

Google Book

Amazon

博客來

Core concepts and methods in load forecasting = with applications in distribution networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Core concepts and methods in load forecasting/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Electric power-plants - Load -
Online resource:	https://doi.org/10.1007/978-3-031-27852-5
ISBN:	9783031278525

Core concepts and methods in load forecasting = with 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

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

3444026
Electric power-plants
--Load

LC Class. No.: TK1005 / .H33 2023

Dewey Class. No.: 621.3121

Core concepts and methods in load forecasting = with applications in distribution networks /
LDR:02763nmm a2200337 a 4500 001 2317422
003 DE-He213
005 20230430133334.0
006 m d
007 cr nn 008maaau
008 230902s2023 sz s 0 eng d
020 $a 9783031278525 $q (electronic bk.)
020 $a 9783031278518 $q (paper)
024 7 $a 10.1007/978-3-031-27852-5 $2 doi
035 $a 978-3-031-27852-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TK1005 $b .H33 2023
072 7 $a TH $2 bicssc
072 7 $a TEC031000 $2 bisacsh
072 7 $a THY $2 thema
082 0 4 $a 621.3121 $2 23
090 $a TK1005 $b .H113 2023
100 1 $a Haben, Stephen. $3 3631494
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.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xv, 331 p. : $b ill. (some col.), digital ; $c 24 cm.
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.
650 0 $a Electric power-plants $x Load $x Forecasting. $3 3444026
650 0 $a Electric power distribution $x Forecasting. $3 3631496
650 0 $a Electric power transmission $x Forecasting. $3 3631497
650 1 4 $a Power Stations. $3 3592504
650 2 4 $a Applied Probability. $3 3599446
650 2 4 $a Electrical Power Engineering. $3 3592498
650 2 4 $a Neural Circuits. $3 3597311
650 2 4 $a Mechanical and Thermal Energy Storage. $3 3591860
650 2 4 $a Control, Robotics, Automation. $3 3592500
700 1 $a Voss, Marcus. $3 3631495
700 1 $a Holderbaum, William. $3 3590884
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-27852-5
950 $a Energy (SpringerNature-40367)