摘要
为了对能源消耗做出精准的预测,文章提出了一种基于带外生变量的季节差分移动自回归(seasonal autoregressive integrated moving average with exogenous,SARIMAX)模型与极限梯度提升算法(extreme gradient boosting,XGBoost)混合模型的能耗预测方法。首先导入实验所需的训练数据以及辅佐用的天气环境数据,利用k-means构建天气簇类,然后构建节假日指示器,根据季节趋势做进一步调整,利用网格搜索选取SARIMAX模型最优参数组合,最后混合XGBoost算法优化预测模型,做出预测并对比实现结果。通过结果分析可知,混合SARIMAX模型和XGBoost模型能够在考虑多个外生变量的基础上实现对区域能耗的精准预测。
Accurate prediction of energy consumption is helpful for further value mining and data fusion.In order to achieve this purpose,this paper proposes an energy consumption prediction method based on SARIMAX(seasonal autoregressive integrated moving average with exogenous)and XGBoost(eXtreme Gradient Boosting algorithm)hybrid model.First we import the training data required for the experiment and the auxiliary weather environment data,compared curve relationships and the correlation coefficient matrix,and used k-means to build weather clusters.Then we built holiday indicators,made further adjustments based on seasonal trends,and used grid search to select the optimal parameter combination of the SARIMAX model.Finally,we fused XGBoost algorithm to optimize the prediction model,made predictions and compared the implementation results.Through the analysis of the results,it can be seen that the hybrid SARIMAX model and the XGBoost model can accurately predict regional energy consumption on the basis of considering multiple exogenous variables.
作者
李国栋
周扬
李凯
LI Guodong;ZHOU Yang;LI Kai(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China;State Grid Xinjiang Information and Telecommunication Company,Urumqi 830000,China)
出处
《电力信息与通信技术》
2022年第3期26-33,共8页
Electric Power Information and Communication Technology
