The integration of a large number of wind farms poses big challenges to the secure and economical operation of power systems, and ultra-short-term wind power forecasting is an effective solution. However, traditional approaches can only predict an individual wind farm power at a time and ignore the spatio-temporal correlation of wind farms. In this paper, a novel ultra-short-term forecasting framework based on spatio-temporal (ST) analysis, multi-output support vector machine (MSVM) and grey wolf optimizer (GWO) which defined ST-GWO-MSVM model is proposed to predict the output wind power from multiple wind farms; the ST-GWO-MSVM model includes data analysis stage, parameters optimization stage, and modeling stage. In the data analysis stage, the person correlation coefficient and partial autocorrelation function are used to analyze the spatio-temporal correlation of wind power. In the parameters optimization stage, to avoid obtaining the unreliable forecasting results due to the parameters are chosen empirically, the GWO algorithm is used to optimize the kernel function parameters of the MSVM model. In the modeling stage, an innovative forecasting model with optimal parameter of MSVM is proposed to predict the output wind power of 15 wind farms. Results show that the performance of ST-GWO-MSVM is better than other benchmark models in terms of multiple-error metrics including fractional bias, direction accuracy, and improvement percentages.

大量风电场的集成给电力系统的安全和经济运行带来了巨大的挑战，超短期风电预测是一种有效的解决方案。但是，传统方法只能一次预测单个风电场的功率，而忽略了风电场的时空相关性。本文提出了一种基于时空分析，多输出支持向量机和灰狼优化器的超短期预报框架，该框架定义了ST-GWO-MSVM模型。预测多个风电场的输出风能；ST-GWO-MSVM模型包括数据分析阶段，参数优化阶段和建模阶段。在数据分析阶段，利用人员相关系数和偏自相关函数分析风电的时空相关性。在参数优化阶段，为避免因经验选择参数而导致获得不可靠的预测结果，采用GWO算法对MSVM模型的核函数参数进行优化。在建模阶段，提出了一种具有最优MSVM参数的创新预测模型来预测15个风电场的输出风功率。结果表明，在包括分数偏差，方向准确性和改进百分比在内的多重误差指标方面，ST-GWO-MSVM的性能优于其他基准模型。为了避免由于经验选择参数而导致获得不可靠的预测结果，使用GWO算法对MSVM模型的核函数参数进行优化。在建模阶段，提出了一种具有最优MSVM参数的创新预测模型来预测15个风电场的输出风功率。结果表明，在包括分数偏差，方向准确性和改进百分比在内的多重误差指标方面，ST-GWO-MSVM的性能优于其他基准模型。为了避免由于经验选择参数而导致获得不可靠的预测结果，采用了GWO算法对MSVM模型的核函数参数进行优化。在建模阶段，提出了一种具有最优MSVM参数的创新预测模型来预测15个风电场的输出风功率。结果表明，在包括分数偏差，方向准确性和改进百分比在内的多重误差指标方面，ST-GWO-MSVM的性能优于其他基准模型。