基于通径分析和相空间重构的光伏发电预测模型

光伏发电的短期预测对电网稳定运行、经济调度和可再生能源调节具有重要意义。但光伏功率输出受辐射强度、温度等气象因素影响,具有较大的波动性和随机性。为了提高预测精度和不同天气类型的普适性,文章提出了一种基于支持向量回归结合相空间重构和相似日选择的混合光伏输出预测算法。采用通径系数分析对历史数据集进行处理,量化光伏出力和气象因子的相关性,并确定主导气象因子作为相似日选择的标准。随后,利用相空间重构技术对非线性光伏功率时间序列进行处理,抑制了原始数据集的混沌特性。用实际数据验证了该算法的预测有效性。结果表明,与传统的支持向量回归模型相比,文中的预测模型可以进一步提高预测精度。此外,文中算法在晴天和阴雨天的情况下都表现出良好的性能。

A hybrid short-term PV power forecast model based on data processing

The short-term forecast of photovoltaic solar power is important for grid stable operation, economic dispatch, and renewable energy accommodation. However, photovoltaic power output is influenced by environmental factors such as radiation intensity and temperature, showing great volatility and randomness. In order to further improve the accuracy of prediction and the universality of different weather types, a hybrid photovoltaic power forecast (HPF) algorithm based on support vector regression combined with phase space reconstruction and similar day selection is proposed in this paper. Using the real data, we verify the prediction effectiveness of the proposed HPF algorithm. The results show that compared with the traditional support vector regression model, the prediction model in this paper can further improve the prediction accuracy. Additionally, the algorithm in this paper also shows good performance under both weak-fluctuation and strong fluctuation.