基于多特征分析和提取的短期光伏功率预测

Short-term Photovoltaic Power Prediction Based on Multi-feature Analysis and Extraction

在对短期光伏发电功率预测时,多维数值天气预报(numerical weather prediction,NWP)数据中存在大量冗余和不相关特征,不仅影响预测的准确度,也会增加模型的复杂度,为此提出一种基于多特征分析和提取的短期光伏功率预测模型。通过K-means++聚类选取与预测日具有相似天气类型的历史数据作为训练样本,利用一阶差分具有滤波的特性对不稳定的特征数据进行处理,同时构造新特征;引入因子分析法,考虑特征与输出功率之间的相关性并提取有效特征,由远少于特征数的公共因子作为预测模型的输入数据;最后采用XGBoost对光伏功率进行预测。对某光伏电站仿真结果表明,提出的预测模型在晴天、晴转多云和阴雨天下的均方根误差分别为5.33%、6.13%和9.5%,在非晴天模式下的预测精度较传统方法可提升3%~10%。研究结果可为复杂天气下的光伏功率预测提供参考。

There are a large number of redundant and irrelevant features in multi-dimensional numerical weather prediction(NWP) data when forecasting short-term photovoltaic power generation, which not only affect the accuracy of the forecast, but also increase the complexity of the model. Therefore, a short-term photovoltaic power prediction model based on multi-feature analysis and extraction was proposed. After using K-means++ clustering to select historical data with similar weather types to the predicted date as training sample, the unstable characteristic data were processed by using the feature of filtering with first difference, and new features were constructed at the same time. Factor analysis method was introduced to extract effective features considering the correlation between features and output power, and the common factors which are far less than the number of features were used as the input data of the prediction model.Finally, XGBoost was used to predict the photovoltaic power. The simulation results of a photovoltaic power station show that the root mean square errors(RMSE) of the proposed prediction model are 5.33%, 6.13% and 9.5% in clear day, clear to overcast day and rainy day, respectively. And the prediction accuracy of the proposed model can be improved by 3%~10% compared with the traditional method in non-sunny days. The research results can provide a reference for photovoltaic power prediction under complex weather.