用于短期电力负荷预测的日负荷特性分类及特征集重构策略  

Daily Load Characteristic Classification and Feature Set Reconstruction Strategy for Short-term Power Load Forecasting

作　　者：徐先峰[1] 赵依 刘状壮 李陇杰 卢勇 XU Xianfeng;ZHAO Yi;LIU Zhuangzhuang;LI Longjie;LU Yong(College of Electronics and Control Engineering,Chang’an University,Xi’an 710064,Shaanxi Province,China)

机构地区：[1]长安大学电子与控制工程学院,陕西省西安市710064

出　　处：《电网技术》2022年第4期1548-1556,共9页Power System Technology

基　　金：陕西省重点研发计划(2021GY-098)。

摘　　要：准确的负荷预测是电力系统安全稳定运行的重要保障。当充分考虑多因素影响,海量输入数据的前端预处理与变量遴选对提高负荷预测精度至关重要。针对传统时间变量信息模糊、维数冗余问题,引入基于余弦相似度的k-means聚类分析技术实现日负荷特性分类,并通过分类标签替代传统时间变量;考虑到负荷与多因素在小时粒度下的耦合关系,提出了基于特征集重构和最大信息系数的特征筛选策略,实现小时粒度的精细化特征筛选;最后引入了具备强大信息挖掘能力的时间卷积网络,实现高精度短期电力负荷预测。实验结果表明,应用提出的上述2个改进策略后,替换低效时间变量和小时粒度的最优特征集使输入数据质量进一步优化,显著提升了多个经典模型的预测性能,而结合改进策略的时间卷积网络模型具有更高的预测精度;且文章方法适用于全年各时段的预测,具备良好的可移植性和鲁棒性。Accurate load forecasting is the important guarantee for the safe and stable operation of a power system.When multiple factors are fully considered,the preprocessing of massive input data and variables selection are essential to improve the accuracy of load forecasting.For the problems like unclear information and dimension redundancy in the traditional time variables,the k-means clustering analysis technology based on cosine similarity is introduced to realize the classification of daily load curves,and the classification labels are used to replace the traditional time variables;Considering the relationship between load and multiple factors at hourly granularity,a feature selection strategy based on the feature set reconstruction and the Maximal Information Coefficient is proposed to achieve a refined feature selection with hourly granularity;The time convolutional network with great learning ability is introduced to achieve high-precision short-term load forecasting.The results show that after applying the former two proposed strategies,the quality of input data are optimized by the replacement of the inefficient time variables and the optimal feature sets with hourly granularity,and the prediction performance of multiple classic models are significantly improve,among which,the time convolutional network model combined with two strategies has a higher accuracy.And also,the proposed strategies are suitable for load forecasting throughout the year,with good portability and robustness.

关 键 词：短期电力负荷预测 时间卷积网络 聚类分析 特征筛选 最大信息系数 

分 类 号：TM721[电气工程—电力系统及自动化]