基于Prophet与XGBoost混合模型的短期负荷预测

准确有效的预测电力负荷对电网的安全稳定运行具有重要的参考价值。通过对Prophet框架和XGBboost (eXtreme gradient boosting)机器学习模型的深度分析，提出了基于Prophet与XGBoost的混合电力负荷预测模型，针对大量的历史电负荷数据、日期信息、气象数据，分别构建Prophet电力负荷预测模型和XGBboost机器学习预测模型，通过误差倒数法将Prophet和XGBoost结合得到混合预测模型。应用所提方法对西南地区某地市历史电负荷数据进行验证，结果证明，Prophet-XGBoost混合模型比支持向量机回归模型(SVR, support vector regression)、Prophet模型和XGBoost模型拥有更高的预测精度，且与SVR模型相比运行时间更短。

Short-Term Power Load Forecasting Based onProphet and XGBoost Mixed Model

Accurate and effective load forecasting is of great importance to secure and stable operation of power grid. Through deep analysis on Prophet frame and extreme gradient boosting (abbr. XGBboost) machine learning model, a hybrid load forecasting model based on Prophet-XGBoost was proposed. Based on large amount of historic load data, data information and meteorological data, a Prophet based load forecasting model and an XGBboost based machine learning forecasting model were constructed respectively, and by means of reciprocal error method the Prophet and XGBoost were combined to obtain hybrid forecasting model. Taking historical load data of a certain city located in Southwest China for example, the proposed hybrid model was applied to the load forecasting test, and the results show that using the proposed Prophet-XGBoost hybrid model a higher forecasting accuracy can be obtained than by such models as support vector machine regression (abbr. SVR,support vector regression) model, Prophet model and XGBoost model, and the time wasted for the load forecasting by the proposed method is much shorter than that by SVR model.