Model of selecting prediction window in ramps forecasting
Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ouyang, Tinghui [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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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.] |
|---|---|
| Übergeordnetes Werk: |
volume:108 ; year:2017 ; pages:98-107 ; extent:10 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.renene.2017.02.035 |
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| Katalog-ID: |
ELV015347311 |
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| 520 | |a Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. | ||
| 520 | |a Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. | ||
| 650 | 7 | |a Prediction time window |2 Elsevier | |
| 650 | 7 | |a Genetic algorithm |2 Elsevier | |
| 650 | 7 | |a Wind power ramp events |2 Elsevier | |
| 650 | 7 | |a Non-ramp data |2 Elsevier | |
| 650 | 7 | |a Ramp prediction |2 Elsevier | |
| 700 | 1 | |a Zha, Xiaoming |4 oth | |
| 700 | 1 | |a Qin, Liang |4 oth | |
| 700 | 1 | |a Xiong, Yi |4 oth | |
| 700 | 1 | |a Huang, Heming |4 oth | |
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10.1016/j.renene.2017.02.035 doi GBVA2017016000018.pica (DE-627)ELV015347311 (ELSEVIER)S0960-1481(17)30121-0 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Ouyang, Tinghui verfasserin aut Model of selecting prediction window in ramps forecasting 2017transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction time window Elsevier Genetic algorithm Elsevier Wind power ramp events Elsevier Non-ramp data Elsevier Ramp prediction Elsevier Zha, Xiaoming oth Qin, Liang oth Xiong, Yi oth Huang, Heming 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:108 year:2017 pages:98-107 extent:10 https://doi.org/10.1016/j.renene.2017.02.035 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 108 2017 98-107 10 045F 530 |
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10.1016/j.renene.2017.02.035 doi GBVA2017016000018.pica (DE-627)ELV015347311 (ELSEVIER)S0960-1481(17)30121-0 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Ouyang, Tinghui verfasserin aut Model of selecting prediction window in ramps forecasting 2017transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction time window Elsevier Genetic algorithm Elsevier Wind power ramp events Elsevier Non-ramp data Elsevier Ramp prediction Elsevier Zha, Xiaoming oth Qin, Liang oth Xiong, Yi oth Huang, Heming 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:108 year:2017 pages:98-107 extent:10 https://doi.org/10.1016/j.renene.2017.02.035 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 108 2017 98-107 10 045F 530 |
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10.1016/j.renene.2017.02.035 doi GBVA2017016000018.pica (DE-627)ELV015347311 (ELSEVIER)S0960-1481(17)30121-0 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Ouyang, Tinghui verfasserin aut Model of selecting prediction window in ramps forecasting 2017transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction time window Elsevier Genetic algorithm Elsevier Wind power ramp events Elsevier Non-ramp data Elsevier Ramp prediction Elsevier Zha, Xiaoming oth Qin, Liang oth Xiong, Yi oth Huang, Heming 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:108 year:2017 pages:98-107 extent:10 https://doi.org/10.1016/j.renene.2017.02.035 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 108 2017 98-107 10 045F 530 |
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10.1016/j.renene.2017.02.035 doi GBVA2017016000018.pica (DE-627)ELV015347311 (ELSEVIER)S0960-1481(17)30121-0 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Ouyang, Tinghui verfasserin aut Model of selecting prediction window in ramps forecasting 2017transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction time window Elsevier Genetic algorithm Elsevier Wind power ramp events Elsevier Non-ramp data Elsevier Ramp prediction Elsevier Zha, Xiaoming oth Qin, Liang oth Xiong, Yi oth Huang, Heming 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:108 year:2017 pages:98-107 extent:10 https://doi.org/10.1016/j.renene.2017.02.035 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 108 2017 98-107 10 045F 530 |
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10.1016/j.renene.2017.02.035 doi GBVA2017016000018.pica (DE-627)ELV015347311 (ELSEVIER)S0960-1481(17)30121-0 DE-627 ger DE-627 rakwb eng 530 620 530 DE-600 620 DE-600 Ouyang, Tinghui verfasserin aut Model of selecting prediction window in ramps forecasting 2017transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. Prediction time window Elsevier Genetic algorithm Elsevier Wind power ramp events Elsevier Non-ramp data Elsevier Ramp prediction Elsevier Zha, Xiaoming oth Qin, Liang oth Xiong, Yi oth Huang, Heming 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:108 year:2017 pages:98-107 extent:10 https://doi.org/10.1016/j.renene.2017.02.035 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 108 2017 98-107 10 045F 530 |
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Ouyang, Tinghui |
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Elektronische Aufsätze |
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Ouyang, Tinghui |
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10.1016/j.renene.2017.02.035 |
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530 620 |
| title_sort |
model of selecting prediction window in ramps forecasting |
| title_auth |
Model of selecting prediction window in ramps forecasting |
| abstract |
Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. |
| abstractGer |
Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. |
| abstract_unstemmed |
Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model. |
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GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Model of selecting prediction window in ramps forecasting |
| url |
https://doi.org/10.1016/j.renene.2017.02.035 |
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true |
| author2 |
Zha, Xiaoming Qin, Liang Xiong, Yi Huang, Heming |
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Zha, Xiaoming Qin, Liang Xiong, Yi Huang, Heming |
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| up_date |
2024-07-06T17:29:24.086Z |
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