Data Pre-processing Method Based on Distorted Data Noise Reduction and Its Application in Wind Power Prediction
Improving the accuracy of wind power data is of great significance for building ubiquitous power internet of things (UPIoT). Wind power prediction has a high demand for historical data sets. Most of research was focused on improving the prediction accuracy by establishing different prediction models...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Editorial Department of Power Generation Technology
2020-08-01
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| Series: | 发电技术 |
| Subjects: | |
| Online Access: | https://www.pgtjournal.com/EN/10.12096/j.2096-4528.pgt.19035 |
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| Summary: | Improving the accuracy of wind power data is of great significance for building ubiquitous power internet of things (UPIoT). Wind power prediction has a high demand for historical data sets. Most of research was focused on improving the prediction accuracy by establishing different prediction models or proposing different prediction algorithms. There is not much attention on noise data elimination. Thus, a noise reduction method for the historical wind power data was proposed, which was mainly applied to the data set, by eliminating the distorted data in the historical wind power data, the amount of useless data was reduced, the accuracy of wind power prediction was improved, and the data modeling and prediction time was shortened. |
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| ISSN: | 2096-4528 |