Xgboost算法在区域用电预测中的应用!  

Application of Xgboost Algorithm in the Regional Electricity Consumption Forecast

作　　者：许裕栗 杨晶 李柠[3] 甘中学 XU Yuli;YANG Jing;LI Ning;GAN Zhongxue(ENN Science and Technology Development Co.,Ltd.,Langfang 065001,China;State Key Laboratory of Coal-Based Low-Carbon Energy,Langfang 065001,China;Key Laboratory of System Control and Information Processing,Ministry of Education,Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]新奥科技发展有限公司,河北廊坊065001 [2]煤基低碳能源国家重点实验室,河北廊坊065001 [3]上海交通大学自动化系系统控制与信息处理教育部重点实验室,上海200240

出　　处：《自动化仪表》2018年第7期1-5,共5页Process Automation Instrumentation

基　　金：国家973计划基金资助项目(2014CB249200)

摘　　要：针对区域用户用电预测的影响因素众多、用户行为模式差异的特点,提出一种基于Xgboost算法的综合预测方法。该方法从周期、趋势、扰动三方面考虑用电的影响因素,搜集用户历史用电数据和气象数据,对数据中的缺失值进行补充,并对数据进行平滑化处理。将所得用户数据进行K-means聚类,从而得到不同类别的用户。运用最大信息系数(MIC)计算各影响因素与用户用电量的相关性大小,依据相关性大小对不同影响因素排序,进而提取主要影响因素。利用Xgboost算法构建不同用户类别的用电预测模型,对不同类别用户进行用电预测,进而获得区域整体用电情况。采用实际用电数据,调整算法参数,对综合方法进行仿真分析,并与其他方法进行对比。结果表明,该方法综合考虑了多方面影响因素,划分了不同的用户群落,具有更高的精度和可靠性。Xgboost算法在区域用电预测中具有较好的应用前景。The forecasting of reginal power consumption is affected by many factors,and the behavior patterns of users are different,in accordance with these features,a comprehensive forecasting method based on Xgboost algorithm is proposed. With this method,the inflrencing factors of power consumption are considered from three aspects,i. e.,cycle,trend and disturbance,the historical power consumption data of users and the meteorological data are collected,then the missing values in these data are completmented,and smoothing process is conducted. The user data obtained are clustered by K-means to get the different categories of the users. Then,the maximal information coefficient（ MIC） is used to calculate the correlation between each influencing factor and the electricity consumption of users. According to the magnitude of the correlation,the influencing factors are ranked,and then the main influencing factors are extracted. The Xgboost algorithm is used to build the electricity prediction model of different user categories,and to make the electricity prediction for different categories of users,so as to get the overall power consumption of the region. The actual power consumption data are used to adjust the algorithm parameters. The comprehensive method is simulated and compared with other methods. The results show that this method comprehensively considers many influencing factors,and divides different user categories and has higher accuracy and reliability. Xgboost algorithm has a good application prospect in regional electricity consumption forecast.

关 键 词：用电预测 K-MEANS 最大信息系数 Xgboost模型 决策树 

分 类 号：TH86[机械工程—仪器科学与技术] TP29[机械工程—精密仪器及机械]