| 引用本文: | 凌武能,杭乃善,李如琦.基于云支持向量机模型的短期风电功率预测[J].电力自动化设备,2013,33(7): |
| LING Wuneng,HANG Naishan,LI Ruqi.Short-term wind power forecasting based on cloud SVM model[J].Electric Power Automation Equipment,2013,33(7): |
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| 摘要: |
| 将云模型和支持向量机（SVM）相结合，提出一种适合短期风电功率预测的云支持向量机模型。该模型采用云变换方法提取风速序列的定性特征，并通过SVM建立风速特征与风电功率间的关系。对未来24 h的风电功率预测结果显示，该模型在某个点上的预测值是一个有稳定倾向的离散值集合。采用逆向云算法求取集合的期望值作为确定性预测结果，并与SVM和自回归求和移动平均（ARIMA）模型的预测结果相比较，结果表明云支持向量机具有更高的预测精度，预测效果显著，因此，该模型可有效应用于短期风电功率预测。 |
| 关键词: 风电 预测 云模型 云变换 支持向量机 |
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| Short-term wind power forecasting based on cloud SVM model |
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LING Wuneng, HANG Naishan, LI Ruqi
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School of Electrical Engineering，Guangxi University，Nanning 530004，China
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| Abstract: |
| A CSVM（Cloud Support Vector Machine） model combining the cloud model and the SVM（Support Vector Machine） is proposed for the short-term wind power forecasting，which applies the cloud transformation to extract the qualitative attribute of wind speed data and uses SVM to build the relationship between wind speed and wind power. The forecasts for the next 24 hours’ wind power show that，the forecasts at a particular point of the presented model is a set of discrete values with stabilized bias. The backward cloud algorithm is applied to calculate the expectation of the forecast set as the deterministic prediction，which is more accurate than that forecasted by SVM model or ARIMA（Auto-Regressive Integrated Moving Average） model. The presented model is effective for short-term wind power forecasting. |
| Key words: wind power forecasting cloud model cloud transformation support vector machines |
