查询结果:   曹梦,刘宝成,何金,张春晖,胡泉伟.基于前趋势相似度的细粒度用户用电负荷预测[J].计算机应用与软件,2018,35(7):158 - 164,172.
中文标题
基于前趋势相似度的细粒度用户用电负荷预测
发表栏目
应用技术与研究
摘要点击数
1023
英文标题
FINE GRAINED USER POWER LOAD FORECASTING BASED ON PRIOR TREND SIMILARITY
作 者
曹梦 刘宝成 何金 张春晖 胡泉伟 Cao Meng Liu Baocheng He Jin Zhang Chunhui Hu Quanwei
作者单位
国网天津市电力科学研究院 天津 300384 国网天津检修公司 天津 300300    
英文单位
State Grid Tianjin Electric Power Research Institute,Tianjin 300384,China State Grid Tianjin Electric Power Company,Tianjin 300300,China    
关键词
用电量预测 动态时间规整 K-mediods BP神经网络 趋势相似度
Keywords
Electricity consumption prediction Dynamic time warping K-mediods BP neural network Trend similarity
基金项目
作者资料
曹梦,工程师,主研领域:高压电。刘宝成,高工。何金,工程师。张春晖,工程师。胡泉伟,工程师。 。
文章摘要
在智能电网普及的大数据背景下,对电力数据进行准确地分析和预测具有重要意义。提出一种基于前趋势相似度的细粒度居民用电预测模型。根据用户的用电行为特征采用基于DTW距离的K-mediods方法对总体用户进行细粒度划分;在各个子类分别建立用电量预测模型;根据用户的用电行为具有周期性突变这一现象,采用基于前趋势相似度的BP神经网络模型对原BP网络进行改进。基于真实居民用电数据的实验表明,所提出的方法具有较好的预测效果。
Abstract
In the context of big data popularizing in smart grids, it is of great significance to accurately analyze and predict power data. Aiming at this problem, a fine-grained residential electricity forecasting model based on the similarity of the previous trend was proposed. According to the characteristics of the user’s electricity consumption, the D-distance-based K-mediods method was used to fine-grain the overall user. In each subcategory, an electricity consumption prediction model was established. For the phenomenon that the user’s electricity consumption had a periodic mutation, the BP neural network model based on the similarity of the previous trend was used to improve the original BP network. Experiments based on real residential electricity data show that the propos method has a good predictive effect.
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