Regional Wind Power Ramp Forecasting through Multinomial Logistic Regression
Regional Wind Power Ramp Forecasting through Multinomial Logistic Regression
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DOI：
10.1109/greentech46478.2020.9289816
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Xiaomei Chen;Jie Zhao;Miao He
中科院分区：
文献类型：
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作者：
Xiaomei Chen;Jie Zhao;Miao He
风电爬坡是风电生产中突然但重大的变化。有关即将发生的风电爬坡顺序的信息可以帮助电力系统运营商及时启动运行或爬坡储备。本文提出了利用实时中尺度风速测量来预测区域风电斜坡水平的新方法。受中尺度风速测量与区域风电数据相关性的启发，该方法利用多项式逻辑回归进行风电斜坡预测。提出了一种以加权方式组合各个回归模型的概率输出的方法，通过最小化组合模型的 Brier 技能得分来计算权重。所提出的方法通过使用真实世界数据进行测试，并与基准方法进行比较。结果揭示了所提出方法的有效性。
Wind power ramps are the abrupt yet significant change in wind power productions. The information on the ordinal levels of impending wind power ramp could help power system operator to arm operation or ramping reserves in a timely manner. This paper presents novel approaches for regional wind power ramp level forecasting using real-time meso-scale wind speed measurements. Motivated by the correlation of the meso-scale wind speed measurements with the regional wind power data, the proposed approach utilizes multinomial logistic regression for wind power ramp forecasting. An approach that combines the probabilistic output of individual regressive models in a weighted manner is proposed, with the weights calculated by minimizing the Brier skill score of the combined model. The proposed methods are tested by using real-world data, and is compared with benchmark methods. The results reveal the effectiveness of the proposed approaches.