Seasonal Analysis and Prediction of Wind Energy Using Random Forests and ARX Model Structures
To effectively utilize wind energy, many learning-based autoregressive models have been proposed in the literature. Improving their short-term prediction accuracy, however, is difficult, which mainly result from the stochastic nature of wind. Moreover, the incorporation of seasonal effects to improv...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lin, Yujie [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on control systems technology - New York, NY : IEEE, 1993, 23(2015), 5, Seite 1994-2002 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2015 ; number:5 ; pages:1994-2002 |
| Links: |
|---|
| DOI / URN: |
10.1109/TCST.2015.2389031 |
|---|
| Katalog-ID: |
OLC1959561340 |
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| 520 | |a To effectively utilize wind energy, many learning-based autoregressive models have been proposed in the literature. Improving their short-term prediction accuracy, however, is difficult, which mainly result from the stochastic nature of wind. Moreover, the incorporation of seasonal effects to improve their accuracies has not been considered, as most reported studies only relied on relatively short data sets. This brief examines meteorological data that were recorded over a six-year period and contrast various model structures and identification methods proposed in the literature. One focus of this brief is the prediction of wind speed and direction, which has not been extensively studied in the literature but is important for grid management. The reported results highlight that an increase in prediction accuracy can be obtained: 1) by incorporating seasonal effects into the model; 2) by including routinely measured variables, such as radiation and pressure; and 3) by separately predicting wind speed and direction. | ||
| 650 | 4 | |a Wind speed | |
| 650 | 4 | |a Autoregressive (AR) data structure | |
| 650 | 4 | |a Predictive models | |
| 650 | 4 | |a meteorological models | |
| 650 | 4 | |a Mathematical model | |
| 650 | 4 | |a Accuracy | |
| 650 | 4 | |a wind direction | |
| 650 | 4 | |a renewable energy | |
| 650 | 4 | |a Analytical models | |
| 650 | 4 | |a Wind forecasting | |
| 650 | 4 | |a Data models | |
| 650 | 4 | |a Meteorology | |
| 700 | 1 | |a Kruger, Uwe |4 oth | |
| 700 | 1 | |a Zhang, Junping |4 oth | |
| 700 | 1 | |a Wang, Qi |4 oth | |
| 700 | 1 | |a Lamont, Lisa |4 oth | |
| 700 | 1 | |a Chaar, Lana El |4 oth | |
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10.1109/TCST.2015.2389031 doi PQ20160617 (DE-627)OLC1959561340 (DE-599)GBVOLC1959561340 (PRQ)c2117-c293eddd4909352d0107187d8713ba2543b75ec0de96505957afbbf7246ec5f0 (KEY)0226256820150000023000501994seasonalanalysisandpredictionofwindenergyusingrand DE-627 ger DE-627 rakwb eng 004 DNB Lin, Yujie verfasserin aut Seasonal Analysis and Prediction of Wind Energy Using Random Forests and ARX Model Structures 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier To effectively utilize wind energy, many learning-based autoregressive models have been proposed in the literature. Improving their short-term prediction accuracy, however, is difficult, which mainly result from the stochastic nature of wind. Moreover, the incorporation of seasonal effects to improve their accuracies has not been considered, as most reported studies only relied on relatively short data sets. This brief examines meteorological data that were recorded over a six-year period and contrast various model structures and identification methods proposed in the literature. One focus of this brief is the prediction of wind speed and direction, which has not been extensively studied in the literature but is important for grid management. The reported results highlight that an increase in prediction accuracy can be obtained: 1) by incorporating seasonal effects into the model; 2) by including routinely measured variables, such as radiation and pressure; and 3) by separately predicting wind speed and direction. Wind speed Autoregressive (AR) data structure Predictive models meteorological models Mathematical model Accuracy wind direction renewable energy Analytical models Wind forecasting Data models Meteorology Kruger, Uwe oth Zhang, Junping oth Wang, Qi oth Lamont, Lisa oth Chaar, Lana El oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 23(2015), 5, Seite 1994-2002 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:23 year:2015 number:5 pages:1994-2002 http://dx.doi.org/10.1109/TCST.2015.2389031 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7027784 http://search.proquest.com/docview/1704217333 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 AR 23 2015 5 1994-2002 |
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10.1109/TCST.2015.2389031 doi PQ20160617 (DE-627)OLC1959561340 (DE-599)GBVOLC1959561340 (PRQ)c2117-c293eddd4909352d0107187d8713ba2543b75ec0de96505957afbbf7246ec5f0 (KEY)0226256820150000023000501994seasonalanalysisandpredictionofwindenergyusingrand DE-627 ger DE-627 rakwb eng 004 DNB Lin, Yujie verfasserin aut Seasonal Analysis and Prediction of Wind Energy Using Random Forests and ARX Model Structures 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier To effectively utilize wind energy, many learning-based autoregressive models have been proposed in the literature. Improving their short-term prediction accuracy, however, is difficult, which mainly result from the stochastic nature of wind. Moreover, the incorporation of seasonal effects to improve their accuracies has not been considered, as most reported studies only relied on relatively short data sets. This brief examines meteorological data that were recorded over a six-year period and contrast various model structures and identification methods proposed in the literature. One focus of this brief is the prediction of wind speed and direction, which has not been extensively studied in the literature but is important for grid management. The reported results highlight that an increase in prediction accuracy can be obtained: 1) by incorporating seasonal effects into the model; 2) by including routinely measured variables, such as radiation and pressure; and 3) by separately predicting wind speed and direction. Wind speed Autoregressive (AR) data structure Predictive models meteorological models Mathematical model Accuracy wind direction renewable energy Analytical models Wind forecasting Data models Meteorology Kruger, Uwe oth Zhang, Junping oth Wang, Qi oth Lamont, Lisa oth Chaar, Lana El oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 23(2015), 5, Seite 1994-2002 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:23 year:2015 number:5 pages:1994-2002 http://dx.doi.org/10.1109/TCST.2015.2389031 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7027784 http://search.proquest.com/docview/1704217333 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 AR 23 2015 5 1994-2002 |
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ddc 004 misc Wind speed misc Autoregressive (AR) data structure misc Predictive models misc meteorological models misc Mathematical model misc Accuracy misc wind direction misc renewable energy misc Analytical models misc Wind forecasting misc Data models misc Meteorology |
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Seasonal Analysis and Prediction of Wind Energy Using Random Forests and ARX Model Structures |
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Seasonal Analysis and Prediction of Wind Energy Using Random Forests and ARX Model Structures |
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Lin, Yujie |
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IEEE transactions on control systems technology |
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10.1109/TCST.2015.2389031 |
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seasonal analysis and prediction of wind energy using random forests and arx model structures |
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Seasonal Analysis and Prediction of Wind Energy Using Random Forests and ARX Model Structures |
| abstract |
To effectively utilize wind energy, many learning-based autoregressive models have been proposed in the literature. Improving their short-term prediction accuracy, however, is difficult, which mainly result from the stochastic nature of wind. Moreover, the incorporation of seasonal effects to improve their accuracies has not been considered, as most reported studies only relied on relatively short data sets. This brief examines meteorological data that were recorded over a six-year period and contrast various model structures and identification methods proposed in the literature. One focus of this brief is the prediction of wind speed and direction, which has not been extensively studied in the literature but is important for grid management. The reported results highlight that an increase in prediction accuracy can be obtained: 1) by incorporating seasonal effects into the model; 2) by including routinely measured variables, such as radiation and pressure; and 3) by separately predicting wind speed and direction. |
| abstractGer |
To effectively utilize wind energy, many learning-based autoregressive models have been proposed in the literature. Improving their short-term prediction accuracy, however, is difficult, which mainly result from the stochastic nature of wind. Moreover, the incorporation of seasonal effects to improve their accuracies has not been considered, as most reported studies only relied on relatively short data sets. This brief examines meteorological data that were recorded over a six-year period and contrast various model structures and identification methods proposed in the literature. One focus of this brief is the prediction of wind speed and direction, which has not been extensively studied in the literature but is important for grid management. The reported results highlight that an increase in prediction accuracy can be obtained: 1) by incorporating seasonal effects into the model; 2) by including routinely measured variables, such as radiation and pressure; and 3) by separately predicting wind speed and direction. |
| abstract_unstemmed |
To effectively utilize wind energy, many learning-based autoregressive models have been proposed in the literature. Improving their short-term prediction accuracy, however, is difficult, which mainly result from the stochastic nature of wind. Moreover, the incorporation of seasonal effects to improve their accuracies has not been considered, as most reported studies only relied on relatively short data sets. This brief examines meteorological data that were recorded over a six-year period and contrast various model structures and identification methods proposed in the literature. One focus of this brief is the prediction of wind speed and direction, which has not been extensively studied in the literature but is important for grid management. The reported results highlight that an increase in prediction accuracy can be obtained: 1) by incorporating seasonal effects into the model; 2) by including routinely measured variables, such as radiation and pressure; and 3) by separately predicting wind speed and direction. |
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Seasonal Analysis and Prediction of Wind Energy Using Random Forests and ARX Model Structures |
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http://dx.doi.org/10.1109/TCST.2015.2389031 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7027784 http://search.proquest.com/docview/1704217333 |
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Kruger, Uwe Zhang, Junping Wang, Qi Lamont, Lisa Chaar, Lana El |
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