基于MA-SVM方法的短期光伏功率预测

光伏发电的功率波动性大，其准确预测对于大规模的光伏发电并网具有重要意义。利用相关性分析法与时间序列方法选取并预测了某电站所在区域的气象数据，得到光伏发电现场更为准确的气象信息预测值。利用主成分分析方法对气象数据降维，得到几种关键影响因子，最终利用改进的支持向量机（SVM）算法对多变量特征序列与光伏功率的关系建模。在验证试验中，使用训练后的支持向量机模型完成预测，并且对预测误差的产生进行了分析。通过与神经网络算法等各种算法的预测效果进行对比，MA SVM方法的误差相对较小，证明了预测的有效性。

Short-Term Photovoltaic Power Prediction Based on MA-SVM Method

Photovoltaic (PV) power has the characteristics of large fluctuation, and its accurate prediction is of great significance for large scale PV power and grid connection. The correlation analysis and time series moving average (MA) methods are used to determine and predict weather data in the region where a power station is located, and a more accurate prediction value of the atmospheric information of PV power generation site is obtained. Principal component analysis is used to reduce dimension of meteorology data, and several key influencing factors are obtained. Finally, the improved support vector machine (SVM) algorithm is used to build the model of the relationship between multi variable feature sequence and PV power. In the verification experiment, the trained SVM model is used to complete the prediction, and the generation of prediction error is analyzed. The prediction effects between the neural network algorithm and the others are compared. The results show that the error of MA SVM method is relatively small, which proves the validity of prediction.