Abstract Accurate prediction for short-term wind speed can reduce the adverse impact of wind farm on power system effectively. To this end, a novel hybrid forecasting approach combining two-layer decomposition, improved hybrid differential evolution-Harris hawks optimization (IHDEHHO), phase space reconstruction (PSR) and kernel extreme learning machine (KELM) is proposed. Primarily, a set of sub-components are obtained by decomposing the collected raw wind speed series with two-layer decomposition strategy. Subsequently, all the sub-components are reconstructed into the corresponding phase space matrixes by PSR, after which the vectors are divided into training, validation and testing sets, respectively. Among the subsets, training set and validation set are applied to establish prediction model and select optimal parameters of KELM. Later, the optimization for arguments within PSR and KELM are synchronously implemented by the proposed IHDEHHO algorithm. Afterward, the final forecast results are deduced by cumulating the forecasting values of all sub-components. Through the application on three datasets collected from Sotavento Galicia (SG) with different prediction horizons and comparison with six related models, it is attested that the proposed hybrid prediction model is effective and suitable for multi-step short-term wind speed forecasting.

中文翻译：

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基于两层分解、改进混合DE-HHO优化和KELM的多步风速预测混合方法

摘要 准确预测短期风速可以有效降低风电场对电力系统的不利影响。为此，提出了一种结合两层分解、改进的混合差分进化-Harris hawks优化(IHDEHHO)、相空间重构(PSR)和核极限学习机(KELM)的新型混合预测方法。首先，通过使用两层分解策略分解收集到的原始风速序列，得到一组子分量。随后，所有子分量通过 PSR 重构为相应的相空间矩阵，然后将向量分别划分为训练集、验证集和测试集。在子集中，训练集和验证集用于建立预测模型和选择 KELM 的最佳参数。之后，PSR 和 KELM 中的参数优化由所提出的 IHDEHHO 算法同步实现。之后，通过累加所有子组件的预测值，推导出最终的预测结果。通过在 Sotavento Galicia (SG) 收集的三个不同预测范围的数据集上的应用，并与六个相关模型的比较，证明所提出的混合预测模型是有效的，适用于多步短期风速预测。