一种基于XGBoost算法的月度负荷预测方法A Monthly Load Forecasting Method Based on XGBoost Algorithm
钱仲文,陈浩,纪德良
QIAN Zhongwen,CHEN Hao,JI Deliang
摘要(Abstract)：
为向大工业提供更为精确的月度负荷预测,提出将XGBoost(极端梯度上升)算法引入电网负荷预测,对负荷及相关影响因素指标进行异常识别填补、指标转换、独热编码等数据预处理工作;结合关联分析,对相关影响因素指标进行筛选;最后采用XGBoost算法进行月度负荷建模预测。结合实例,对比XGBoost与支持向量机、神经网络模型算法在大工业用户近几年历史月度负荷数据建模预测中的应用,发现XGBoost模型应用效果较佳,具有一定的实用性。
In order to provide more accurate monthly load forecasting for large industries, XGBoost(extreme gradient rise) algorithm is introduced into load forecasting of power grid, and the data preprocessing work such as anomaly identification, index conversion and unique heat coding is carried out; the related influencing factor indexes are screened by combining the correlation analysis; Finally, XGBoost algorithm is used for modelling and forecasting of monthly load. In an example, by comparing the application of XGBoost with that of support vector machine and neural network model algorithm in the past monthly load forecasting in the last years, it is found that XGBoost model has better application effect and practicability.
关键词(KeyWords)：
XGBoost;支持向量机;神经网络;月度负荷预测;数据预处理;关联分析
XGBoost;support vector machine;neural network;monthly load forecasting;data preprocessing;correlation analysis
基金项目(Foundation): 国网浙江省电力有限公司科技项目(5211HZ17000C)
作者(Author):
钱仲文,陈浩,纪德良
QIAN Zhongwen,CHEN Hao,JI Deliang
DOI: 10.19585/j.zjdl.201905012
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