谢从珍;王江储;谢心昊;刘智健;白剑锋;

• 1:华南理工大学电力学院

摘要(Abstract)：

光伏出力与天气模式密切相关,深度挖掘天气模式特征信息能有效提高光伏出力预测精度。由于信息粒度的粗细程度对光伏出力预测的精度有影响,使用粗粒度的原始特征或聚类特征的传统光伏出力预测方法在预测精度方面存在提升空间。针对以上问题,提出了一种基于细粒度特征的贝叶斯优化梯度提升树(Bayesianoptimizationalgorithm gradient boosting decision tree, BOA-GBDT)光伏出力预测方法,该方法首先对日间每条气象监测数据及光伏出力监测数据构建细粒度特征,包括瞬时天气模式特征及时窗趋势性特征,然后采用贝叶斯优化算法(Bayesianoptimization algorithm,BOA)对细粒度特征的种类进行约减,最后通过(gradient boosting decision tree,GBDT)模型拟合特征与光伏曲线的关系,建立BOA-GBDT光伏出力预测模型。对实际算例进行误差分析,结果表明相比传统支持向量机(support vector machine,SVM)方法,该方法构建的预测模型运行时间平均减少97.3%,均方根误差平均减少80.4%。验证了该方法的有效性。

关键词(KeyWords)： 光伏出力预测;天气特征;特征工程;GBDT

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 谢从珍;王江储;谢心昊;刘智健;白剑锋;

Email:

DOI: 10.13335/j.1000-3673.pst.2019.0447

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