IDHNet: Ultra‐Short‐Term Wind Power Forecasting With IVMD–DCInformer–HSSA Network
ABSTRACT The variability and unpredictability of wind power generation present significant challenges for grid management and planning. Enhancing the accuracy of wind power forecasting is crucial for improving the reliability of renewable energy systems. To enhance the accuracy of temporal wind powe...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | Energy Science & Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ese3.1968 |
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| Summary: | ABSTRACT The variability and unpredictability of wind power generation present significant challenges for grid management and planning. Enhancing the accuracy of wind power forecasting is crucial for improving the reliability of renewable energy systems. To enhance the accuracy of temporal wind power predictions, the IVMD–DCInformer–HSSA framework has been introduced. Initially, the original wind power data is decomposed into multiple intrinsic mode function (IMF) components using the improved variational mode decomposition (IVMD) technique. Subsequently, the Sparrow search algorithm (HSSA) is employed to optimize the parameters of the enhanced Informer deep neural network, which are then integrated into the improved Informer model. The predictions of each IMF component resulting from the IVMD decomposition are then combined to generate the final prediction outcome. The experimental results show that the R‐squared value of the proposed combined model is increased to 0.9903, and the accuracy is increased by 1%–3% compared with other models, which has a good prediction effect. |
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| ISSN: | 2050-0505 |