Renewable energy forecasting : from models to applications
1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather pred...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kariniotakis, Georges [herausgeberIn] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Duxford, United Kingdom: Woodhead Publishing, an imprint of Elsevier ; 2017 |
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| Schlagwörter: |
Renewable energy sources, Forecasting TECHNOLOGY & ENGINEERING ; Power Resources ; Alternative & Renewable |
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| Formangabe: |
Electronic books |
| Anmerkung: |
Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 |
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| Umfang: |
Online Ressource |
| Reproduktion: |
Online-Ausg. |
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| Reihe: |
Woodhead Publishing Series in Energy |
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| Links: | |
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| ISBN: |
978-0-08-100505-7 0-08-100505-9 |
| Katalog-ID: |
892971436 |
|---|
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| 245 | 1 | 0 | |a Renewable energy forecasting |b from models to applications |c edited by George Kariniotakis |
| 264 | 1 | |a Duxford, United Kingdom |b Woodhead Publishing, an imprint of Elsevier |c 2017 | |
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| 500 | |a Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 | ||
| 520 | |a 1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing | ||
| 520 | |a 1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence | ||
| 520 | |a 2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models | ||
| 520 | |a 3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios | ||
| 520 | |a Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models | ||
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9780081005057 : electronic bk. 978-0-08-100505-7 0081005059 : electronic bk. 0-08-100505-9 9780081005040 0081005040 (DE-627)892971436 (DE-576)517991144 (DE-599)GBV892971436 (OCoLC)990802650 (OCoLC)990802650 (ELSEVIER)ocn990802650 (EBP)014125331 DE-627 eng DE-627 rda eng XA-GB TJ808 TEC031010 bisacsh TEC 031010 bisacsh TEC 031010 bisacsh Renewable energy forecasting from models to applications edited by George Kariniotakis Duxford, United Kingdom Woodhead Publishing, an imprint of Elsevier 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Woodhead Publishing Series in Energy Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing 1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence 2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models 3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models Online-Ausg. Renewable energy sources Forecasting Renewable energy sources Forecasting Renewable energy sources Forecasting TECHNOLOGY & ENGINEERING ; Power Resources ; Alternative & Renewable Renewable energy sources ; Forecasting Electronic books Electronic books Kariniotakis, Georges herausgeberin edt 9780081005040 Erscheint auch als Druck-Ausgabe Renewable energy forecasting Duxford, United Kingdom : Woodhead Publishing, an imprint of Elsevier, [2017] 9780081005040 0081005040 http://www.sciencedirect.com/science/book/9780081005040 X:ELSEVIER Verlag Volltext https://www.sciencedirect.com/science/book/9780081005040 X:ELSEVIER Verlag lizenzpflichtig https://external.dandelon.com/download/attachments/dandelon/ids/AT002E96C7CF182D80BC1C125816200430AEC.pdf V:DE-601 X:AGI pdf/application 2017-12-02 Verlag Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGY 2017 ZDB-33-ESD GBV-33-EBS-HST GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP GBV_ILN_105 ISIL_DE-841 GBV_ILN_120 ISIL_DE-715 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2111 ISIL_DE-944 BO 045F 333.794 23 01 0830 175566009X ACQ i z 26-02-18 105 01 0841 4074510847 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 120 01 0715 3494461414 OLR-33-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 17-07-19 120 02 0715 3577617306 00 --%%-- --%%-- g --%%-- alma z 21-01-20 185 01 3519 4514689270 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540289810 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. 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9780081005057 : electronic bk. 978-0-08-100505-7 0081005059 : electronic bk. 0-08-100505-9 9780081005040 0081005040 (DE-627)892971436 (DE-576)517991144 (DE-599)GBV892971436 (OCoLC)990802650 (OCoLC)990802650 (ELSEVIER)ocn990802650 (EBP)014125331 DE-627 eng DE-627 rda eng XA-GB TJ808 TEC031010 bisacsh TEC 031010 bisacsh TEC 031010 bisacsh Renewable energy forecasting from models to applications edited by George Kariniotakis Duxford, United Kingdom Woodhead Publishing, an imprint of Elsevier 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Woodhead Publishing Series in Energy Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing 1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence 2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models 3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models Online-Ausg. Renewable energy sources Forecasting Renewable energy sources Forecasting Renewable energy sources Forecasting TECHNOLOGY & ENGINEERING ; Power Resources ; Alternative & Renewable Renewable energy sources ; Forecasting Electronic books Electronic books Kariniotakis, Georges herausgeberin edt 9780081005040 Erscheint auch als Druck-Ausgabe Renewable energy forecasting Duxford, United Kingdom : Woodhead Publishing, an imprint of Elsevier, [2017] 9780081005040 0081005040 http://www.sciencedirect.com/science/book/9780081005040 X:ELSEVIER Verlag Volltext https://www.sciencedirect.com/science/book/9780081005040 X:ELSEVIER Verlag lizenzpflichtig https://external.dandelon.com/download/attachments/dandelon/ids/AT002E96C7CF182D80BC1C125816200430AEC.pdf V:DE-601 X:AGI pdf/application 2017-12-02 Verlag Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGY 2017 ZDB-33-ESD GBV-33-EBS-HST GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP GBV_ILN_105 ISIL_DE-841 GBV_ILN_120 ISIL_DE-715 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2111 ISIL_DE-944 BO 045F 333.794 23 01 0830 175566009X ACQ i z 26-02-18 105 01 0841 4074510847 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 120 01 0715 3494461414 OLR-33-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 17-07-19 120 02 0715 3577617306 00 --%%-- --%%-- g --%%-- alma z 21-01-20 185 01 3519 4514689270 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540289810 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2111 02 DE-944 4046047011 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 23 01 0830 Elsevier EBook http://www.sciencedirect.com/science/book/9780081005040 105 01 0841 http://www.sciencedirect.com/science/book/9780081005040 120 01 0715 http://www.sciencedirect.com/science/book/9780081005040 120 02 0715 http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991014916762903501 185 01 3519 http://www.sciencedirect.com/science/book/9780081005040 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780081005040 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780081005040 23 01 0830 2020-01654, 2020-01655, 2020-01656, 2020-01657, 2020-01658 23 01 0830 ACQ 23 01 0830 olr-else2 23 01 0830 olr-else 105 01 0841 OLR-ELV-TEST 120 01 0715 OLR-33-EBS 120 02 0715 alma 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier 23 01 0830 2017.05.17 |
| allfields_unstemmed |
9780081005057 : electronic bk. 978-0-08-100505-7 0081005059 : electronic bk. 0-08-100505-9 9780081005040 0081005040 (DE-627)892971436 (DE-576)517991144 (DE-599)GBV892971436 (OCoLC)990802650 (OCoLC)990802650 (ELSEVIER)ocn990802650 (EBP)014125331 DE-627 eng DE-627 rda eng XA-GB TJ808 TEC031010 bisacsh TEC 031010 bisacsh TEC 031010 bisacsh Renewable energy forecasting from models to applications edited by George Kariniotakis Duxford, United Kingdom Woodhead Publishing, an imprint of Elsevier 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Woodhead Publishing Series in Energy Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing 1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence 2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models 3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models Online-Ausg. Renewable energy sources Forecasting Renewable energy sources Forecasting Renewable energy sources Forecasting TECHNOLOGY & ENGINEERING ; Power Resources ; Alternative & Renewable Renewable energy sources ; Forecasting Electronic books Electronic books Kariniotakis, Georges herausgeberin edt 9780081005040 Erscheint auch als Druck-Ausgabe Renewable energy forecasting Duxford, United Kingdom : Woodhead Publishing, an imprint of Elsevier, [2017] 9780081005040 0081005040 http://www.sciencedirect.com/science/book/9780081005040 X:ELSEVIER Verlag Volltext https://www.sciencedirect.com/science/book/9780081005040 X:ELSEVIER Verlag lizenzpflichtig https://external.dandelon.com/download/attachments/dandelon/ids/AT002E96C7CF182D80BC1C125816200430AEC.pdf V:DE-601 X:AGI pdf/application 2017-12-02 Verlag Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGY 2017 ZDB-33-ESD GBV-33-EBS-HST GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP GBV_ILN_105 ISIL_DE-841 GBV_ILN_120 ISIL_DE-715 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2111 ISIL_DE-944 BO 045F 333.794 23 01 0830 175566009X ACQ i z 26-02-18 105 01 0841 4074510847 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 120 01 0715 3494461414 OLR-33-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 17-07-19 120 02 0715 3577617306 00 --%%-- --%%-- g --%%-- alma z 21-01-20 185 01 3519 4514689270 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540289810 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2111 02 DE-944 4046047011 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 23 01 0830 Elsevier EBook http://www.sciencedirect.com/science/book/9780081005040 105 01 0841 http://www.sciencedirect.com/science/book/9780081005040 120 01 0715 http://www.sciencedirect.com/science/book/9780081005040 120 02 0715 http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991014916762903501 185 01 3519 http://www.sciencedirect.com/science/book/9780081005040 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780081005040 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780081005040 23 01 0830 2020-01654, 2020-01655, 2020-01656, 2020-01657, 2020-01658 23 01 0830 ACQ 23 01 0830 olr-else2 23 01 0830 olr-else 105 01 0841 OLR-ELV-TEST 120 01 0715 OLR-33-EBS 120 02 0715 alma 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier 23 01 0830 2017.05.17 |
| allfieldsGer |
9780081005057 : electronic bk. 978-0-08-100505-7 0081005059 : electronic bk. 0-08-100505-9 9780081005040 0081005040 (DE-627)892971436 (DE-576)517991144 (DE-599)GBV892971436 (OCoLC)990802650 (OCoLC)990802650 (ELSEVIER)ocn990802650 (EBP)014125331 DE-627 eng DE-627 rda eng XA-GB TJ808 TEC031010 bisacsh TEC 031010 bisacsh TEC 031010 bisacsh Renewable energy forecasting from models to applications edited by George Kariniotakis Duxford, United Kingdom Woodhead Publishing, an imprint of Elsevier 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Woodhead Publishing Series in Energy Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing 1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence 2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models 3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models Online-Ausg. Renewable energy sources Forecasting Renewable energy sources Forecasting Renewable energy sources Forecasting TECHNOLOGY & ENGINEERING ; Power Resources ; Alternative & Renewable Renewable energy sources ; Forecasting Electronic books Electronic books Kariniotakis, Georges herausgeberin edt 9780081005040 Erscheint auch als Druck-Ausgabe Renewable energy forecasting Duxford, United Kingdom : Woodhead Publishing, an imprint of Elsevier, [2017] 9780081005040 0081005040 http://www.sciencedirect.com/science/book/9780081005040 X:ELSEVIER Verlag Volltext https://www.sciencedirect.com/science/book/9780081005040 X:ELSEVIER Verlag lizenzpflichtig https://external.dandelon.com/download/attachments/dandelon/ids/AT002E96C7CF182D80BC1C125816200430AEC.pdf V:DE-601 X:AGI pdf/application 2017-12-02 Verlag Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGY 2017 ZDB-33-ESD GBV-33-EBS-HST GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP GBV_ILN_105 ISIL_DE-841 GBV_ILN_120 ISIL_DE-715 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2111 ISIL_DE-944 BO 045F 333.794 23 01 0830 175566009X ACQ i z 26-02-18 105 01 0841 4074510847 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 120 01 0715 3494461414 OLR-33-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 17-07-19 120 02 0715 3577617306 00 --%%-- --%%-- g --%%-- alma z 21-01-20 185 01 3519 4514689270 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540289810 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2111 02 DE-944 4046047011 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 23 01 0830 Elsevier EBook http://www.sciencedirect.com/science/book/9780081005040 105 01 0841 http://www.sciencedirect.com/science/book/9780081005040 120 01 0715 http://www.sciencedirect.com/science/book/9780081005040 120 02 0715 http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991014916762903501 185 01 3519 http://www.sciencedirect.com/science/book/9780081005040 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780081005040 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780081005040 23 01 0830 2020-01654, 2020-01655, 2020-01656, 2020-01657, 2020-01658 23 01 0830 ACQ 23 01 0830 olr-else2 23 01 0830 olr-else 105 01 0841 OLR-ELV-TEST 120 01 0715 OLR-33-EBS 120 02 0715 alma 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier 23 01 0830 2017.05.17 |
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9780081005057 : electronic bk. 978-0-08-100505-7 0081005059 : electronic bk. 0-08-100505-9 9780081005040 0081005040 (DE-627)892971436 (DE-576)517991144 (DE-599)GBV892971436 (OCoLC)990802650 (OCoLC)990802650 (ELSEVIER)ocn990802650 (EBP)014125331 DE-627 eng DE-627 rda eng XA-GB TJ808 TEC031010 bisacsh TEC 031010 bisacsh TEC 031010 bisacsh Renewable energy forecasting from models to applications edited by George Kariniotakis Duxford, United Kingdom Woodhead Publishing, an imprint of Elsevier 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Woodhead Publishing Series in Energy Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing 1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence 2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models 3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models Online-Ausg. 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1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing 1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence 2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models 3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 |
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1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing 1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence 2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models 3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 |
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1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing 1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence 2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models 3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed June 27, 2017 |
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Woodhead Publishing Series in Energy Renewable Energy Forecasting From Models to Applications Edited By George Kariniotakis WP WOODHEAD PUBLISHING An imprint of Elsevier Contents List of contributors xi Part One Introduction to meteorology and measurement technologies … Principles of meteorology and numerical weather prediction Sue Ellen Haupt, Pedro A. Jimenez, Jared A. Lee, Branko Kosovic … Introduction to meteorology for renewable energy forecasting … Observational data and assimilation into numerical weather prediction models … Configuring numerical weather prediction to the needs of the problem … Postprocessing … Probabilistic forecasting … Planning for validation … Weather forecasting as a Big Data problem Acknowledgments References Further reading … Measurement methodologies for wind energy based on ground-level remote sensing … Sveh-Erik Gryning, Torben Mikkelsen, Christophe Baehr, Alain Dabas, Paula Gomez, Ewan O'Connor, Lucie Rottner, Mikael Sjdholm, Irene Suomi, Nikola Vasiljevic … Introduction References … Part Two Methods for renewable energy forecasting … Wind power forecasting—a review of the state of the art Gregor Giebel, George Kariniotakis … Introduction Time series models Meteorological modeling for wind power predictions Short-term prediction models with NWPs … Contents vi … Upscaling models Spatio-temporal forecasting Ramp forecasting Variability forecasting Uncertainty of wind power predictions The ANEMOS projects and other major R&D activities Conclusions Glossary Acknowledgments References Mathematical methods for optimized solar forecasting … Hugo T.C. Pedro, Rich H. Inman, Carlos F.M. Coimbra … Introduction Regression methods Artificial intelligence techniques Hybrid systems State of the art in solar forecasting Conclusions References Short-term forecasting based on all-sky cameras … Andreas Kazantzidis, Panagiotis Tzoumanikas, Philippe Blanc, Pierre Massip, Stefan Wilbert, Lourdes Ramirez-Santigosa … Introduction Sky camera systems Image processing techniques Geometrical calibration of all-sky cameras Case study: solar resource and forecasting methodology in the frame of DNICast project … Conclusions and future trends References … Short-term solar power forecasting based on satellite images … Philippe Blanc, Jan Remund, Loic Vallance … Introduction … Surface solar irradiance retrieval from meteorological geostationary satellite … Different approaches for satellite-based forecasting … Conclusion and perspectives Acronyms References … Contents … vii Wave energy forecasting … Gordon Reikard … Introduction Characteristics of the data The physics models Statistical and time series models Physics versus statistics Wave energy converters Simulating wave farms Conclusions References Forecasting intrahourly variability of wind generation … Claire L. Vincent, Pierre-Julien Trombe … Introduction Meteorological causes of hour-scale wind variability Observing hour-scale wind variability Forecasting wind power variability Correlation between wind fluctuations at spatially distributed sites Using wind variability information to improve wind farm operations and scheduling … Conclusions and future developments Acknowledgments References … Characterization of forecast errors and benchmarking of renewable energy forecasts … Stefano Alessandrini, Simone Sperati … Introduction ANEMOS benchmark WIRE benchmark Discussion and conclusions References … Part Three Applications of forecasting to power system management and markets … Wind power in electricity markets and the value of forecasting … Nicolo Mazzi, Pierre Pinson … Introduction Electricity market context From market revenue to forecast value Formulation of offering strategies … Contents … Test case exemplification … Overall conclusions and perspectives References … Forecasting and setting power system operating reserves … Manuel Matos, Ricardo Bessa, Audun Botterud, Zhi Zhou … Introduction … Integration of uncertainty forecasts in operating reserve estimation … Conceptual frameworks … Illustrative results … A look into the future Acknowledgments References … Forecasting for storage management … Edgardo D. Castronuovo … Introduction … Mathematical formulations … Coordinating renewable producers and hydro-pumped storage for optimal participation in the market … The cooperation of renewable producers and storage facilities in isolated systems … Storage sizing … Reliability studies of the combined operation of wind power plants and storage facilities … Conclusion Acknowledgment References … Dynamic line rating forecasting … Romain Dupin, Andrea Michiorri … Introduction Dynamic line rating calculations Dynamic line rating devices Dynamic line rating forecasts Benefits of dynamic line rating forecasting Conclusions References … Contents ix … The role of predictability in the investment phase of wind farms … Javier Sanz Rodrigo, Laura Frias Paredes, Robin Girard, George Kariniotakis, Kevin Laquaine, Nicole Stoffels, Lueder von Bremen … Introduction A model chain for wind resource and predictability assessment The value of predictability in spatial planning Conclusions References Index … |
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code="c">XA-GB</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TJ808</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">TEC031010</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">TEC 031010</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">TEC 031010</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Renewable energy forecasting</subfield><subfield code="b">from models to applications</subfield><subfield code="c">edited by George Kariniotakis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Duxford, United Kingdom</subfield><subfield code="b">Woodhead Publishing, an imprint of Elsevier</subfield><subfield code="c">2017</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield 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assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar 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