| 考虑历史相似性加权的超短期风电功率组合预测 |
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| 引用本文： | 仲悟之,李崇钢,崔杨,李芳,王丹丹.考虑历史相似性加权的超短期风电功率组合预测[J].太阳能学报,2022,43(6):160-168. |
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| 作者姓名： | 仲悟之 李崇钢 崔杨 李芳 王丹丹 |
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| 作者单位： | 1.电网安全与节能国家重点实验室（中国电力科学研究院有限公司）,北京 100192; 2.现代电力系统仿真控制与绿色电能新技术教育部重点实验室（东北电力大学）,吉林 132012 |
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| 基金项目： | 电网安全与节能国家重点实验室（中国电力科学研究院有限公司）开放基金（FXB51202001567） |
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| 摘 要： | 提出一种基于历史相似性加权的超短期风电功率组合预测方法。首先,采用数值天气预报数据、风电历史数据分别作为极限学习机、长短期记忆网络的输入特征并产生预测数据;然后,通过加权灰色关联算法提取与待预测点特征近似的历史数据,评估并校正两类预测模型的预测结果。采用美国科罗拉多州某风电场实际运行数据进行训练与验证,并使用不同加权方法进行对比。结果表明,基于历史相似性优化方法可改善预测效果,缩小预测误差分布范围,验证了该文方法的有效性。
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| 关 键 词： | 风电 预测 深度学习 历史相似性 熵权法 |
| 收稿时间： | 2021-03-24 |
COMBINED PREDICTION OF ULTRA-SHORT TERM WIND POWER CONSIDERING WEIGHTED HISTORICAL SIMILARITY |
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| Zhong Wuzhi,Li Chonggang,Cui Yang,Li Fang,Wang Dandan.COMBINED PREDICTION OF ULTRA-SHORT TERM WIND POWER CONSIDERING WEIGHTED HISTORICAL SIMILARITY[J].Acta Energiae Solaris Sinica,2022,43(6):160-168. |
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| Authors: | Zhong Wuzhi Li Chonggang Cui Yang Li Fang Wang Dandan |
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| Affiliation: | 1. State Key Laboratory of Power Grid Security and Energy Conservation（China Electric Power Research Institute）, Beijing 100192, China; 2. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education（Northeast Electric Power University）, Jilin 132012, China |
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| Abstract: | Accurate and reliable wind power forecasting is of great significance for improving the wind power consumption rate of power systems. Reasonably combining numerical weather forecast data is an effective means to improve the accuracy of wind power forecasting. This paper proposes a method to improve the ultra-short-term prediction accuracy of long- short-term memory networks based on historical similarity. Firstly, the numerical weather forecast is used as the characteristic of the extreme learning machine to generate correction data; then, through the weighted gray correlation algorithm, the historical data that is similar to the feature of the point to be predicted is extracted, and the prediction results of the long-short-term memory network are evaluated and corrected. The calculation example uses the actual operation data of a wind farm in Colorado, USA for training and verification, and uses different correction methods for comparison. The results show that the optimization method based on historical similarity can improve the prediction effect of the short-term memory network and reduce the error fluctuation range, and verified the method in this article. |
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| Keywords: | wind power forecast deep learning historical similarity entropy weight method |
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