基于XGBoost算法的新型短期负荷预测模型研究

针对目前电网在负荷预测中所采集到的数据普遍存在着特征维度较少；特征关系不明；有效数据量较少的特点,为了提高电网短期负荷预测精度,本文提出一种基于XGBoost算法的新型负荷预测模型。基于XGBoost算法的负荷预测模型采用CART树作为基学习器,输入预处理后的历史负荷和特征数据,通过构建多个弱学习器逐层训练模型并得到模型,最后向模型输入测试集特征得到最终的预测结果。本文所搭建的负荷预测模型具有避免对数据特征的标准化、处理字段缺失的数据、不用关心特征间是否相互依赖、学习效果好的优点。根据真实电网数据实验结果,基于XGBoost算法的负荷预测平均绝对误差百分比下降到3.46%,比本文所对比的基于BP、GRNN、DBN神经网络的负荷模型预测值精度更高,表明本文所提模型的优越性。

A new short-term load forecasting based on XGBoost algorithm

At present, there are generally few feature dimensions, unclear relationship among different features, and small effective data volume to the data collected in load forecasting research. In order to improve the accuracy of short-term load forecasting, a new load forecasting model based on XGBoost algorithm is proposed. The load prediction model based on XGBoost algorithm uses CART tree as the basic learner, inputs the preprocessed historical load and characteristic data, then builds several weak learner, layer by layer trains models to get the model, and finally inputs the features of test set into model to get the final predicted results. The load forecasting model established in this paper has the advantages of avoiding the standardization of data features, processing data missing fields, not caring whether the features are interdependent or not, and good learning effect. According to the experimental results of real power grid data, the mean absolute percent error of load prediction based on XGBoost algorithm reaches 3.46%, which is more accurate than the predicted value of load model based on BP, GRNN and DBN neural network, indicating the superiority of the proposed model.