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Core concepts and methods in load fo...
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Haben, Stephen.
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Core concepts and methods in load forecasting = with applications in distribution networks /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Core concepts and methods in load forecasting/ by Stephen Haben, Marcus Voss, William Holderbaum.
其他題名:
with applications in distribution networks /
作者:
Haben, Stephen.
其他作者:
Voss, Marcus.
出版者:
Cham :Springer International Publishing : : 2023.,
面頁冊數:
xv, 331 p. :ill. (some col.), digital ;24 cm.
內容註:
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
標題:
Electric power-plants - Load -
電子資源:
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 forecasting
with 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 /
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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.
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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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