| 摘要: |
| 针对风电功率的高随机和强波动性，提出一种基于EMD-SA-SVR的风电功率超短期预测方法。采用经验模态分解(Empirical Mode Decomposition, EMD)提取风电功率序列的不同特征。将原始序列分解为多个更具规律的模态，针对每个模态序列建立各自的预测模型，以消除不同特征之间的相互影响。鉴于支持向量回归(Support Vector Regression, SVR)好的泛化能力，研究建立基于SVR的各模态预测模型。进一步采用模拟退火(Simulated Annealing, SA)算法对SVR参数进行优化以解决模型选择的多极值复杂非线性问题，获得各模态分量的最优模型，进而汇总各模态分量的结果得到风电功率预测值。在某风电场历史数据上的对比分析表明，EMD-SA-SVR模型可以有效提高风电功率超短期预测精度。 |
| 关键词: 风电功率 超短期预测 经验模态分解 支持向量回归 模拟退火 |
| DOI：10.19783/j.cnki.pspc.190449 |
| 投稿时间：2019-04-24修订日期：2019-08-09 |
| 基金项目:国家自然科学基金项目资助(U1504617) |
|
| On ultra-short-term wind power prediction based on EMD-SA-SVR |
| ZHAO Qian,HUANG Jingtao |
| (College of Electrical Engineering, Henan University of Science and Technology, Luoyang 471023, China) |
| Abstract: |
| An ultra-short-term wind power prediction method based on EMD-SA-SVR is proposed for the high randomness and strong fluctuation. Empirical Mode Decomposition (EMD) is used to extract different features lying in wind power sequence. By decomposing the original sequence into several more regular modes, the accurate prediction model can be built easily for each mode to eliminate the coupling between different features in the original sequence. The prediction models are constructed with Support Vector Regression (SVR) for its fine generalization performance. The Simulated Annealing (SA) algorithm is used to optimize the SVR parameters to solve the multi-extreme complex nonlinear problem of model selection, and the optimized model can be obtained for each mode. The wind power prediction value can be gained by summarizing the results of every single mode. The comparative analyses on a wind farm history data show that the EMD-SA-SVR model can effectively improve the accuracy of ultra-short-term wind power prediction. This work is supported by National Natural Science Foundation of China (No. U1504617). |
| Key words: wind power ultra-short-term prediction empirical mode decomposition (EMD) support vector regression (SVR) simulated annealing (SA) |
