Research on a short-term photovoltaic power prediction method based on CatBoost
The intermittent and fluctuating generation power of PV power plants has an increasingly prominent impact on the safe， stable， and economical operation of power grids. Therefore， it is required to continuously improve the accuracy of PV power prediction to provide accurate information for flexible g...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
zhejiang electric power
2023-02-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=655c0626-ad60-49a0-8f03-6ad260e14292 |
| Summary: | The intermittent and fluctuating generation power of PV power plants has an increasingly prominent impact on the safe， stable， and economical operation of power grids. Therefore， it is required to continuously improve the accuracy of PV power prediction to provide accurate information for flexible grid dispatching and planning. First， the prediction algorithm， characteristic equation， prediction process， and evaluation index of short-term PV generation power are introduced. Afterward， the features constructed in the training set are analyzed and filtered using the SHAP， and the training is performed using the CatBoost. Finally， by comparing the prediction accuracy with other machine learning algorithm models using the same features， the paper indicates that the proposed method can improve the prediction performance and confirms the advantages of the CatBoost that incorporates multidimensional feature models in PV power prediction. |
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| ISSN: | 1007-1881 |