基于空间相似度和深度学习的中长期用电量预测
TM715+.1; 准确地预测用户中长期用电量对电力系统优化与调度具有重要意义.为此,提出一种根据用电曲线空间相似性划分用电用户类别,进而利用深度网络预测单类用户中长期用电量的方法,用以提升预测精度.首先,计算用电数据间的动态时间规整距离,基于规整距离利用层次聚类法绘制层次聚类树对用户分类.然后,通过离差标准化约束分类后每类用户数据的取值范围,进而通过深度神经网络建立单类用户的中长期用电量预测模型.最后,通过实例分析了传统方法与所提方法的用户聚类效果,并对比单类用户的总体、个体用电量预测结果,证明了所提依据空间形状相似度指标可较准确地划分用户类别,提升了中长期用电量的预测精度....
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| Published in | 浙江电力 Vol. 40; no. 5; pp. 45 - 52 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
国网浙江省电力有限公司绍兴供电公司,浙江 绍兴 312000
2021
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-1881 |
| DOI | 10.19585/j.zjdl.202105007 |
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| Abstract | TM715+.1; 准确地预测用户中长期用电量对电力系统优化与调度具有重要意义.为此,提出一种根据用电曲线空间相似性划分用电用户类别,进而利用深度网络预测单类用户中长期用电量的方法,用以提升预测精度.首先,计算用电数据间的动态时间规整距离,基于规整距离利用层次聚类法绘制层次聚类树对用户分类.然后,通过离差标准化约束分类后每类用户数据的取值范围,进而通过深度神经网络建立单类用户的中长期用电量预测模型.最后,通过实例分析了传统方法与所提方法的用户聚类效果,并对比单类用户的总体、个体用电量预测结果,证明了所提依据空间形状相似度指标可较准确地划分用户类别,提升了中长期用电量的预测精度. |
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| AbstractList | TM715+.1; 准确地预测用户中长期用电量对电力系统优化与调度具有重要意义.为此,提出一种根据用电曲线空间相似性划分用电用户类别,进而利用深度网络预测单类用户中长期用电量的方法,用以提升预测精度.首先,计算用电数据间的动态时间规整距离,基于规整距离利用层次聚类法绘制层次聚类树对用户分类.然后,通过离差标准化约束分类后每类用户数据的取值范围,进而通过深度神经网络建立单类用户的中长期用电量预测模型.最后,通过实例分析了传统方法与所提方法的用户聚类效果,并对比单类用户的总体、个体用电量预测结果,证明了所提依据空间形状相似度指标可较准确地划分用户类别,提升了中长期用电量的预测精度. |
| Author | 刘理峰 张永建 林海峰 章剑光 |
| AuthorAffiliation | 国网浙江省电力有限公司绍兴供电公司,浙江 绍兴 312000 |
| AuthorAffiliation_xml | – name: 国网浙江省电力有限公司绍兴供电公司,浙江 绍兴 312000 |
| Author_FL | ZHANG Yongjian LIU Lifeng LIN Haifeng ZHANG Jianguang |
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| Keywords | 用户聚类 动态时间规整 深度网络 用电量预测 |
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