Using the analog ensemble method as a proxy measurement for wind power predictability
Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shahriari, M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Umfang: |
13 |
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| Übergeordnetes Werk: |
Enthalten in: Technologies and practice of CO - HU, Yongle ELSEVIER, 2019, an international journal : the official journal of WREN, The World Renewable Energy Network, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:146 ; year:2020 ; pages:789-801 ; extent:13 |
| Links: |
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| DOI / URN: |
10.1016/j.renene.2019.06.132 |
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ELV048448508 |
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| 520 | |a Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. | ||
| 520 | |a Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. | ||
| 650 | 7 | |a Wind output volatility |2 Elsevier | |
| 650 | 7 | |a Wind power forecasting |2 Elsevier | |
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| 650 | 7 | |a Analog ensemble |2 Elsevier | |
| 650 | 7 | |a Forecast uncertainty |2 Elsevier | |
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| 700 | 1 | |a Clemente-Harding, L. |4 oth | |
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10.1016/j.renene.2019.06.132 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000808.pica (DE-627)ELV048448508 (ELSEVIER)S0960-1481(19)30966-8 DE-627 ger DE-627 rakwb eng Shahriari, M. verfasserin aut Using the analog ensemble method as a proxy measurement for wind power predictability 2020transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind output volatility Elsevier Wind power forecasting Elsevier Probabilistic forecast Elsevier Analog ensemble Elsevier Forecast uncertainty Elsevier Cervone, G. oth Clemente-Harding, L. oth Delle Monache, L. oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:146 year:2020 pages:789-801 extent:13 https://doi.org/10.1016/j.renene.2019.06.132 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 146 2020 789-801 13 |
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10.1016/j.renene.2019.06.132 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000808.pica (DE-627)ELV048448508 (ELSEVIER)S0960-1481(19)30966-8 DE-627 ger DE-627 rakwb eng Shahriari, M. verfasserin aut Using the analog ensemble method as a proxy measurement for wind power predictability 2020transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind output volatility Elsevier Wind power forecasting Elsevier Probabilistic forecast Elsevier Analog ensemble Elsevier Forecast uncertainty Elsevier Cervone, G. oth Clemente-Harding, L. oth Delle Monache, L. oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:146 year:2020 pages:789-801 extent:13 https://doi.org/10.1016/j.renene.2019.06.132 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 146 2020 789-801 13 |
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10.1016/j.renene.2019.06.132 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000808.pica (DE-627)ELV048448508 (ELSEVIER)S0960-1481(19)30966-8 DE-627 ger DE-627 rakwb eng Shahriari, M. verfasserin aut Using the analog ensemble method as a proxy measurement for wind power predictability 2020transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind output volatility Elsevier Wind power forecasting Elsevier Probabilistic forecast Elsevier Analog ensemble Elsevier Forecast uncertainty Elsevier Cervone, G. oth Clemente-Harding, L. oth Delle Monache, L. oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:146 year:2020 pages:789-801 extent:13 https://doi.org/10.1016/j.renene.2019.06.132 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 146 2020 789-801 13 |
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10.1016/j.renene.2019.06.132 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000808.pica (DE-627)ELV048448508 (ELSEVIER)S0960-1481(19)30966-8 DE-627 ger DE-627 rakwb eng Shahriari, M. verfasserin aut Using the analog ensemble method as a proxy measurement for wind power predictability 2020transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind output volatility Elsevier Wind power forecasting Elsevier Probabilistic forecast Elsevier Analog ensemble Elsevier Forecast uncertainty Elsevier Cervone, G. oth Clemente-Harding, L. oth Delle Monache, L. oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:146 year:2020 pages:789-801 extent:13 https://doi.org/10.1016/j.renene.2019.06.132 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 146 2020 789-801 13 |
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10.1016/j.renene.2019.06.132 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000808.pica (DE-627)ELV048448508 (ELSEVIER)S0960-1481(19)30966-8 DE-627 ger DE-627 rakwb eng Shahriari, M. verfasserin aut Using the analog ensemble method as a proxy measurement for wind power predictability 2020transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Wind output volatility Elsevier Wind power forecasting Elsevier Probabilistic forecast Elsevier Analog ensemble Elsevier Forecast uncertainty Elsevier Cervone, G. oth Clemente-Harding, L. oth Delle Monache, L. oth Enthalten in Elsevier Science HU, Yongle ELSEVIER Technologies and practice of CO 2019 an international journal : the official journal of WREN, The World Renewable Energy Network Amsterdam [u.a.] (DE-627)ELV002723662 volume:146 year:2020 pages:789-801 extent:13 https://doi.org/10.1016/j.renene.2019.06.132 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 146 2020 789-801 13 |
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| author |
Shahriari, M. |
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Shahriari, M. Elsevier Wind output volatility Elsevier Wind power forecasting Elsevier Probabilistic forecast Elsevier Analog ensemble Elsevier Forecast uncertainty Using the analog ensemble method as a proxy measurement for wind power predictability |
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Using the analog ensemble method as a proxy measurement for wind power predictability Wind output volatility Elsevier Wind power forecasting Elsevier Probabilistic forecast Elsevier Analog ensemble Elsevier Forecast uncertainty Elsevier |
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Elsevier Wind output volatility Elsevier Wind power forecasting Elsevier Probabilistic forecast Elsevier Analog ensemble Elsevier Forecast uncertainty |
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Using the analog ensemble method as a proxy measurement for wind power predictability |
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10.1016/j.renene.2019.06.132 |
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using the analog ensemble method as a proxy measurement for wind power predictability |
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Using the analog ensemble method as a proxy measurement for wind power predictability |
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Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. |
| abstractGer |
Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. |
| abstract_unstemmed |
Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. |
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Using the analog ensemble method as a proxy measurement for wind power predictability |
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Cervone, G. Clemente-Harding, L. Delle Monache, L. |
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