A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy
A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy
复制标题
基于多尺度主导成分混沌分析、KELM和同步优化策略的多步风速预报混合方法
DOI：
10.3390/su11061804
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发表时间：
2019-03-25
期刊：
影响因子：
3.9
通讯作者：
Chen, Tie
中科院分区：
文献类型：
--
作者：
Fu, Wenlong;Wang, Kai;Chen, Tie
准确的风速预测对电力系统的合理调度和安全运行具有重要意义。然而，由于风速时间序列的非线性和非平稳性，很难建立准确的预测模型。为此，提出了一种多尺度主导成分混沌分析与核极端学习机(KELM)相结合的基于同步优化策略的混合模型，其中多尺度主导成分混沌分析集成了变分模式分解(VMD)、奇异谱分析(SSA)和相空间重构(PSR)。针对这种混合结构，利用改进的混合灰狼优化-正弦余弦算法(IHGWOSCA)对VMD、SSA、PSR和KELM中影响预测性能的参数进行了同步优化。首先，利用VMD将原始风速数据分解为一组不同频率尺度的子序列。然后，通过SSA对每个子序列进行主导成分和残差成分的提取，然后将所有残差成分与VMD残差累加，生成一个额外的预测成分。在此基础上，利用PSR方法求出各分量的KELM的输入和输出，以此建立预测模型。最后，将各分量的预测结果进行累加，得到最终的原始风速预测值。此外，选择了来自Sotavento Galicia(SG)风电场的四个数据集，以实现对所提出模型的性能评估。在此基础上，进行了六个相关模型的比较分析。结果表明，VMD-SSA-PSR-KELM混合模型比其他组合模型具有更好的性能，而基于同步参数优化策略的模型比单独优化的VMD-SSA-PSR-KELM模型平均提高25%。
Accurate wind speed prediction plays a significant role in reasonable scheduling and the safe operation of the power system. However, due to the non-linear and non-stationary traits of the wind speed time series, the construction of an accuracy forecasting model is difficult to achieve. To this end, a novel synchronous optimization strategy-based hybrid model combining multi-scale dominant ingredient chaotic analysis and a kernel extreme learning machine (KELM) is proposed, for which the multi-scale dominant ingredient chaotic analysis integrates variational mode decomposition (VMD), singular spectrum analysis (SSA) and phase-space reconstruction (PSR). For such a hybrid structure, the parameters in VMD, SSA, PSR and KELM that would affect the predictive performance are optimized by the proposed improved hybrid grey wolf optimizer-sine cosine algorithm (IHGWOSCA) synchronously. To begin with, VMD is employed to decompose the raw wind speed data into a set of sub-series with various frequency scales. Later, the extraction of dominant and residuary ingredients for each sub-series is implemented by SSA, after which, all of the residuary ingredients are accumulated with the residual of VMD, to generate an additional forecasting component. Subsequently, the inputs and outputs of KELM for each component are deduced by PSR, with which the forecasting model could be constructed. Finally, the ultimate forecasting values of the raw wind speed are calculated by accumulating the predicted results of all the components. Additionally, four datasets from Sotavento Galicia (SG) wind farm have been selected, to achieve the performance assessment of the proposed model. Furthermore, six relevant models are carried out for comparative analysis. The results illustrate that the proposed hybrid framework, VMD-SSA-PSR-KELM could achieve a better performance compared with other combined models, while the proposed synchronous parameter optimization strategy-based model could achieve an average improvement of 25% compared to the separated optimized VMD-SSA-PSR-KELM model.