中文摘要：

目前光伏发电预测普遍采用采样间隔较大的单一时间尺度功率序列建模，模型简单但对功率时序特征的模拟精度不高。文中提出了一种基于小采样间隔光伏功率数据的多维时间序列局部预测方法。通过构造不同时间尺度的光伏功率均值序列，形成以小时平均光伏功率序列为主要研究序列的多维时间序列；基于相关性分析、C-C方法和嵌入维最小预测误差法确定多维时间序列相空间重构的时间延迟和嵌入维；采用支持向量回归方法建立提前1h 的光伏功率局部预测模型。以国内某微网的光伏功率预测为例进行仿真实验，计算结果表明，多维时间序列局部预测模型优于基于单一时间尺度功率序列的局部预测模型，更具应用价值。

英文摘要：

Currently power series with single time scale and large sampling intervals are generally used in modeling photovoltaic generation forecast.Simple as the model is,its simulation accuracy of the time-series characteristics of photovoltaic （PV） power series is not high.In order to solve this problem,this paper proposes a local forecasting method for multidimensional time-series based on PV power series with small sampling interval.By constructing the main value series of PV power with different time-scales,the multidimensional time-series with hourly average PV power series as the main series is obtained.The correlation analysis,C-C method and minimum prediction error method of embedding dimension are used to compute the time-delay and embedding dimensions of the reconstructed phase space of the multidimensional time-series.The 1-hour ahead local forecasting model for PV power is developed by using support vector regression after phase space reconstruction. To demonstrate the effectiveness,the model is applied and tested in a microgrid.Simulation results show that the proposed local forecasting model based on multidimensional time-series outperforms the local forecasting model based on one-dimensional series,hence it has a better application value.