黄学勤,杨鹏举,赵耀,高少炜
HUANG Xueqin,YANG Pengju,ZHAO Yao,GAO Shaowei

• 1:上海电力大学电气工程学院
• 2:国网上海市电力公司金山供电公司

摘要(Abstract)：

现有基于预测误差的风电功率区间预测通常只考虑风速的影响，在建模过程中忽略了时间影响的问题。对此，提出一种结合N-HiTS（神经分层插值时间序列预测）点预测模型与GAM（广义可加模型）-DVQR(D-vine分位数回归)理论的风电功率区间预测模型。首先，采用N-HiTS对风电功率进行点预测并得到预测误差；然后，构建预测误差的DVQR模型，通过P样条GAM引入时间变量，对Copula参数对应的相关系数进行建模，得到预测误差的条件分位数；最后，根据误差的条件中位数对点预测值进行修正，并叠加误差条件分位数，得到风电的区间预测结果。以中国山西省某风电场实际数据集为例，验证了所提方法的有效性与优越性。
Existing wind power interval forecasting models based on forecasting errors typically consider only the influence of wind speed while neglecting the temporal effects in the modeling process. To address these challenges, this paper proposes a wind power interval forecasting model that combines the point forecasting model of neural hierarchical interpolation for time series forecasting(N-HiTS) with the theory of generalized additive model(GAM)-DVine copula based quantile regression(DVQR). First, the N-HiTS is employed to obtain point predictions of wind power and corresponding forecasting errors. Then, a DVQR model for the forecasting errors is constructed, where temporal variables are incorporated via P-splines GAM to model the correlation coefficients corresponding to Copula parameters, thereby obtaining conditional quantiles of the forecasting errors. Finally, the point predictions are corrected based on the conditional median of errors, and forecasting intervals are generated by superimposing the conditional quantiles of errors. Validation using actual operational data from a wind farm in Shanxi Province, China demonstrates the effectiveness and superiority of the proposed method.

关键词(KeyWords)： 风电功率预测;广义可加模型;分位数回归;区间预测
wind power forecasting;GAM;quantile regression;interval forecasting

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(52377111)

作者(Author): 黄学勤,杨鹏举,赵耀,高少炜
HUANG Xueqin,YANG Pengju,ZHAO Yao,GAO Shaowei

DOI: 10.19585/j.zjdl.202601007

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