Improve the reliability of wind speed forecasting is a crucial task in wind power generation. Due to the stochastic and noise nature of wind, a preprocessing step is beneficial for wind speed series to get clean data. The decomposition technique is reported as the critical preprocessor to transform the unstable wind speed data into several regular components. This study proposes a hybrid forecasting system, which combines secondary decomposition algorithm and optimized back propagation (BP) neural network. For the decomposition part, the variational mode decomposition (VMD) is firstly used to extract the low-frequency part from the original wind data. Then the symplectic geometry mode decomposition (SGMD) decomposes the rest high-frequency part into clean and separate components. The BP algorithm is optimized by the differential evolution (DE) as the predictor in this study. Empirical studies with different comparison models are conducted on real wind speed data. The results affirm the competitive strength of the proposed combination strategy. And the proposed two-stage decomposition technique is applicable for nonlinear wind speed analysis.

提高风速预测的可靠性是风力发电的一项关键任务。由于风的随机性和噪声特性，预处理步骤有利于风速系列获得干净的数据。据报道，分解技术是将不稳定的风速数据转换为几个规则分量的关键预处理器。本研究提出了一种混合预测系统，它结合了二次分解算法和优化的反向传播（BP）神经网络。对于分解部分，首先使用变分模式分解（VMD）从原始风数据中提取低频部分。然后辛几何模式分解 (SGMD) 将其余的高频部分分解为干净且独立的分量。BP算法通过差分进化（DE）作为本研究的预测器进行优化。对真实风速数据进行了不同比较模型的实证研究。结果证实了所提出的组合策略的竞争实力。并且所提出的两阶段分解技术适用于非线性风速分析。