基于k-shape_STL的用户短期用电负荷预测模型
User Short-Term Power Load Forecasting Model Based on k-shape_STL
刘红菊 1班浩然 2刘红艳 3梁宏涛1
作者信息
- 1. 青岛科技大学 信息科学技术学院,山东 青岛 266061
- 2. 浪潮集团有限公司,山东 济南 250000
- 3. 山东女子学院,山东 济南 250002
- 折叠
摘要
为挖掘复杂环境因素对电力负荷预测效果的影响,提高电力负荷预测精确度,提出了一种基于k-shape时间序列聚类与STL季节趋势分解算法相结合的负荷曲线聚类预测模型(k-shape-seasonal and trend decomposition using loess-gradient boosting decision tree,k-shape-STL-GBDT).首先分析用户用电时序特征,利用k-shape时间序列聚类算法根据负荷曲线划分用户聚类,其次,使用STL算法将不同簇的负荷数据划分为季节项、趋势项与随机项.然后,结合温度、湿度等影响因素搭建预测模型,以麻省大学smart*可再生能源项目的公开数据集为例进行分析,并与多种主流聚类分解预测模型进行对比.结果表明新提出的模型框架MAPE减少了4%以上,针对短期负荷预测表现出了较好的性能与预测精度.
Abstract
To excavate the influence of complex environmental factors on power load forecas-ting,improve the accuracy of power load forecasting,a load curve clustering forecasting model based on time series clustering and seasonal trend decomposition algorithm(k-shape-STL-GBDT)is proposed.Firstly,the time series characteristics of users'electricity con-sumption are analyzed,and the k-shape algorithm is used to divide the user clusters accord-ing to the load curve.Secondly,the STL algorithm is used to divide the load data of different clusters into seasonal items,trend items and resid items.Then,we build a prediction model combining the influencing factors such as temperature and humidity and take the public data set of the UMass smart* renewable energy project as an example,and compare it with a va-riety of mainstream clustering decomposition prediction models.The results show that the newly proposed model framework MAPE reduces by more than 4%,and shows better per-formance and prediction accuracy for short-term load forecasting.
关键词
负荷预测/k-shape/STL/趋势项/气象因素Key words
load forecasting/k-shape/STL/trend/meteorological factor引用本文复制引用
基金项目
国家自然科学基金(61973180)
山东省产教融合研究生联合培养示范基地项目(2020-19)
出版年
2024
NETL
NSTL
万方数据