| 引用本文: | 汪 欣,蔡 旭,李 征.结合交叉局部异常因子和注意力机制的
超短期风电功率预测方法[J].电力系统保护与控制,2020,48(23):92-99.[点击复制] |
| WANG Xin,CAI Xu,LI Zheng.Ultra-short-term wind power forecasting method based on a cross LOF preprocessing algorithm and an attention mechanism[J].Power System Protection and Control,2020,48(23):92-99[点击复制] |
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| 摘要: |
| 风电样本数据的质量和风功率预测模型的结构直接影响风电功率预测的精度，提出一种结合交叉局部异常因子(Local Outlier Factor, LOF)和注意力机制的高精度超短期风电功率预测方法。通过交叉LOF算法进行分钟级的风电数据异常孤立点检测，有效提高了样本数据的质量。通过增加注意力机制避免长短期记忆(Long Short-Term Memory, LSTM)算法在编解码过程中固定长度向量导致的数据特性损失问题，从而更有效利用历史数据的特征，提高风功率预测的精度。最后，对真实风场实测数据进行实验分析，验证了所述方法的可行性与准确性。 |
| 关键词: 风电功率预测 局部异常因子 数据预处理 注意力机制 |
| DOI：DOI: 10.19783/j.cnki.pspc.191591 |
| 投稿时间：2019-12-24修订日期：2020-02-16 |
| 基金项目:国家自然科学基金资助(51677117)；山东省重点研发计划资助(2019JZZY020804) |
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| Ultra-short-term wind power forecasting method based on a cross LOF preprocessing algorithm and an attention mechanism |
| WANG Xin,CAI Xu,LI Zheng |
| (1. Wind Power Research Center, Shanghai Jiao Tong University, Shanghai 200240, China;
2. School of Information Science and Technology, Donghua University, Shanghai 201620, China) |
| Abstract: |
| The quality of wind power sample data and the structure of a wind power prediction model directly affect the accuracy of wind power prediction. A high-precision ultra-short-term wind power prediction method combining cross Local Outlier Factor (LOF) and an attention mechanism is proposed. Outlier detection is of wind power data at minute level by a cross LOF algorithm. This effectively improves the quality of sample data to avoid the loss of data characteristics caused by a fixed length vector in the encoding and decoding process of the Long Short Term Memory (LSTM) algorithm. An attention mechanism is added, so that the characteristics of historical data can be used more effectively to improve the accuracy of wind power prediction. Finally, the feasibility and accuracy of the method proposed are verified by the experimental analysis of real wind field measured data.
This work is supported by National Natural Science Foundation of China (No. 51677117) and Key Research and Development Program of Shandong Province (No. 2019JZZY020804). |
| Key words: wind power prediction local outlier factor data preprocessing attention mechanism |
