Short-term photovoltaic power prediction combination model considering seasonal characteristic and data window
The intermittency and randomness of photovoltaic power present different characteristics due to seasonal variations, so it is important to consider seasonal characteristics to improve the accuracy of photovoltaic power prediction. Therefore, a short-term photovoltaic power prediction combination mod...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Power Engineering Technology
2025-01-01
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| Series: | 电力工程技术 |
| Subjects: | |
| Online Access: | https://www.epet-info.com/dlgcjsen/article/abstract/240202096 |
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| Summary: | The intermittency and randomness of photovoltaic power present different characteristics due to seasonal variations, so it is important to consider seasonal characteristics to improve the accuracy of photovoltaic power prediction. Therefore, a short-term photovoltaic power prediction combination model considering seasonal characteristic and data window is proposed in the paper. Firstly, the Pearson correlation coefficient method is adopted to determine suitable meteorological factors with high contribution to photovoltaic power and reduce the input feature dimensions of the prediction model. Secondly, the prediction error of different photovoltaic power models is compared, and the two models with the lowest photovoltaic power prediction error and the lowest correlation are selected to construct the combination model, i.e., gated recurrent unit (GRU) model and extreme gradient boosting (XGboost) model. Thirdly, the effects of different input windows in the historical meteorological data on the prediction accuracy of GRU-XGboost model are analyzed to determine the optimal data window. Finally, on this basis, GRU and XGboost predict the photovoltaic power respectively. The final prediction is obtained by weighted combination of the two predictions. Simulation results show that the proposed model has stronger adaptability and higher prediction accuracy than other models. |
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| ISSN: | 2096-3203 |