| 引用本文: | 叶小岭,陈 浩,郭晓杰,等.基于风速升降特征的短期风电功率预测[J].电力系统保护与控制,2016,44(19):56-62.[点击复制] |
| YE Xiaoling,CHEN Hao,GUO Xiaojie,et al.Short-term wind power prediction based on the UP-DOWN-features of wind speed[J].Power System Protection and Control,2016,44(19):56-62[点击复制] |
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| 摘要: |
| 为提高短期风电功率预测精度，提出了基于风速升降特征的短期风电功率预测方法。该方法分析风速上升或下降对风力发电的影响，根据风速升降特征，为风速添加标记值，增加训练样本维度，从而提高功率预测精度。用上海某风电场2014年9月至2015年9月数据对算法进行验证，并对比最小二乘支持向量机(LSSVM)、极限学习机(ELM)、遗传BP神经网络(GA-BP)三种方法的预测结果。实验结果表明，在风电功率预测中引入风速升降特征能够明显提高了模型的预测精度，适合风电场的短期功率预测。 |
| 关键词: 短期风电功率预测 风速升降特征 特征值 LSSVM ELM GA-BP |
| DOI：10.7667/PSPC151812 |
| 投稿时间：2015-10-15修订日期：2015-12-05 |
| 基金项目:江苏省高校优势学科建设工程资助项目(PAPD);江苏省六大人才高峰项目(WLW-021);中国气象局软科学研究课题(SK20120146)共同资助 |
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| Short-term wind power prediction based on the UP-DOWN-features of wind speed |
| YE Xiaoling,CHEN Hao,GUO Xiaojie,DENG Hua,WANG Yachen |
(Nanjing University of Information Science & Technology, Nanjing 210044, China;Collaborative Innovation
Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of
Information Science & Technology, Nanjing 210044, China) |
| Abstract: |
| In order to improve the accuracy of short-term wind power prediction, a short-term wind power prediction method based on UP-DOWN-features of wind speed is presented. By analyzing the change of output power caused by UP-DOWN-features, wind speed is labeled by the features in every moment to increase the training dimensions and then the prediction accuracy is improved. The data from a wind farm in Shanghai from September 2014 to September 2015 is used. By comparing the prediction results of Least Squares Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), Genetic Algorithms-BP Neural Networks (GA-BP), the prediction accuracy of the model which has added the UP-DOWN-features will be enhanced, and experiments show that it is suitable for short-term wind power prediction. |
| Key words: short-term wind power prediction UP-DOWN-features LSSVM ELM GA-BP |
