| 引用本文: | 赵凌云,刘友波,沈晓东,等.基于CEEMDAN和改进时间卷积网络的短期风电功率预测模型[J].电力系统保护与控制,2022,50(1):42-50.[点击复制] |
| ZHAO Lingyun,LIU Youbo,SHEN Xiaodong,et al.Short-term wind power prediction model based on CEEMDAN and an improved time convolutional network[J].Power System Protection and Control,2022,50(1):42-50[点击复制] |
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| 摘要: |
| 近年来，风力发电逐渐成为可再生能源发电的关键部分。为了提高风力发电功率短期预测的准确度，提出了一种将自适应噪声完备集成经验模态分解与改进时间卷积网络结合的短期风电功率预测模型。首先，利用CEEMDAN对风电功率序列进行分解，得到子序列分量，并分别与关键气象变量数据构成训练集。然后，使用基于时间模式注意力机制的时间卷积网络对子序列分量分别进行预测。最后，重构预测结果后得到最终的预测值。整个预测过程有助于精准刻画风电的分量特性，并通过TPA机制捕捉变量间的关联性，有效地提高风电功率的预测准确率。 |
| 关键词: 风电功率预测 自适应噪声完备集成经验模态分解 时间卷积网络 时间模式注意力机制 |
| DOI：DOI: 10.19783/j.cnki.pspc.210252 |
| 投稿时间：2021-03-09修订日期：2021-05-11 |
| 基金项目:国家自然科学基金项目资助(51977133)；国家自然科学基金重点项目资助(U2066209) |
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| Short-term wind power prediction model based on CEEMDAN and an improved time convolutional network |
| ZHAO Lingyun,LIU Youbo,SHEN Xiaodong,LIU Daiyong,LÜ Shuang |
| (1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China;
2. Deyang Power Supply Company, State Grid Chengdu Electric Power Company, Deyang 618000, China;
3. Chengdu Power Supply Company, State Grid Chengdu Electric Power Company, Chengdu 610000, China) |
| Abstract: |
| In recent years, wind power has gradually become a key part of renewable energy generation. In this paper, an effective short-term wind power forecasting combination model is proposed by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved temporal convolutional network (TCN) to improve the accuracy of short-term wind power prediction. First, CEEMDAN is used to decompose the wind power series to obtain the subsequence components, and the subsequence components are combined with the data of key meteorological variables to form the training set. Then, the time convolution network based on temporal pattern attention (TPA) is used to predict the subsequence components, and the final prediction value is obtained after reconstructing the prediction results. The whole prediction process helps to accurately describe the component characteristics of wind power, and capture the correlation between variables through the TPA mechanism, and this effectively improves the prediction accuracy of wind power.
This work is supported by the National Natural Science Foundation of China (No. 51977133). |
| Key words: wind power prediction complete total empirical mode decomposition based on adaptive white noise time convolution network time mode attention mechanism |
