Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy
The quality of wind data from the numerical weather prediction significantly influences the accuracy of wind power forecasting systems for wind parks. Therefore, an in-depth investigation of these wind data themselves is essential to improve wind power generation efficiency and maintain grid reliabi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722015499 |
| _version_ | 1797900674738946048 |
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| author | Hao Chen Qixia Zhang Yngve Birkelund |
| author_facet | Hao Chen Qixia Zhang Yngve Birkelund |
| author_sort | Hao Chen |
| collection | DOAJ |
| description | The quality of wind data from the numerical weather prediction significantly influences the accuracy of wind power forecasting systems for wind parks. Therefore, an in-depth investigation of these wind data themselves is essential to improve wind power generation efficiency and maintain grid reliability. This paper proposes a novel framework based on machine learning for concurrently analyzing and forecasting predictive errors, called residuals, of wind speed and direction from a numerical weather prediction model versus measurements over a while. The performance of the framework is testified by a wind farm inside the Arctic. It is demonstrated that the residuals still contain significant meteorological information and can be effectively predicted with machine learning and the linear autoregression works well for multi-timesteps predictions of overall, East–West, and North–South wind speeds residuals by comparing the four forecast learning algorithms’ performance. The predictions may be applied to correct the NWP wind model, making quality feedback improvements for inputs for wind power forecasting systems. |
| first_indexed | 2024-04-10T08:48:39Z |
| format | Article |
| id | doaj.art-08ddac77de1f46889f6a283271084c74 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T08:48:39Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-08ddac77de1f46889f6a283271084c742023-02-22T04:31:22ZengElsevierEnergy Reports2352-48472022-11-018661668Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energyHao Chen0Qixia Zhang1Yngve Birkelund2Department of Technology and Safety, UiT the Arctic University of Norway, Tromsø 9019, Norway; Arctic Centre for Sustainable Energy, UiT the Arctic University of Norway, Tromsø 9019, Norway; Corresponding author at: Department of Technology and Safety, UiT the Arctic University of Norway, Tromsø 9019, Norway.Department of Computer Science, UiT the Arctic University of Norway, Tromsø 9019, Norway; Arctic Centre for Sustainable Energy, UiT the Arctic University of Norway, Tromsø 9019, NorwayDepartment of Physics and Technology, UiT the Arctic University of Norway, Tromsø 9019, Norway; Arctic Centre for Sustainable Energy, UiT the Arctic University of Norway, Tromsø 9019, NorwayThe quality of wind data from the numerical weather prediction significantly influences the accuracy of wind power forecasting systems for wind parks. Therefore, an in-depth investigation of these wind data themselves is essential to improve wind power generation efficiency and maintain grid reliability. This paper proposes a novel framework based on machine learning for concurrently analyzing and forecasting predictive errors, called residuals, of wind speed and direction from a numerical weather prediction model versus measurements over a while. The performance of the framework is testified by a wind farm inside the Arctic. It is demonstrated that the residuals still contain significant meteorological information and can be effectively predicted with machine learning and the linear autoregression works well for multi-timesteps predictions of overall, East–West, and North–South wind speeds residuals by comparing the four forecast learning algorithms’ performance. The predictions may be applied to correct the NWP wind model, making quality feedback improvements for inputs for wind power forecasting systems.http://www.sciencedirect.com/science/article/pii/S2352484722015499Autoregressive forecastMachine learningResidual analysisStatistical inferenceWind energyFeedback |
| spellingShingle | Hao Chen Qixia Zhang Yngve Birkelund Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy Energy Reports Autoregressive forecast Machine learning Residual analysis Statistical inference Wind energy Feedback |
| title | Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy |
| title_full | Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy |
| title_fullStr | Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy |
| title_full_unstemmed | Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy |
| title_short | Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy |
| title_sort | machine learning forecasts of scandinavian numerical weather prediction wind model residuals with control theory for wind energy |
| topic | Autoregressive forecast Machine learning Residual analysis Statistical inference Wind energy Feedback |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722015499 |
| work_keys_str_mv | AT haochen machinelearningforecastsofscandinaviannumericalweatherpredictionwindmodelresidualswithcontroltheoryforwindenergy AT qixiazhang machinelearningforecastsofscandinaviannumericalweatherpredictionwindmodelresidualswithcontroltheoryforwindenergy AT yngvebirkelund machinelearningforecastsofscandinaviannumericalweatherpredictionwindmodelresidualswithcontroltheoryforwindenergy |