Dynamic clustering of residential electricity consumption time series data based on Hausdorff distance

Benitez Ignacio; Diez Jose-Luis; Quijano Alfredo; Delgado Ignacio

首页> 外文期刊>Electric power systems research >Dynamic clustering of residential electricity consumption time series data based on Hausdorff distance

【24h】

Dynamic clustering of residential electricity consumption time series data based on Hausdorff distance

机译：基于Hausdorff距离的居民用电时间序列数据的动态聚类

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

As the analysis of electrical loads is reaching data measured from low voltage power distribution networks, there is a need for the main agents involved in the operation and management of the power grids to segment the end users as a function of their shapes of daily energy consumption or load profiles, and to obtain patterns that allow to classify the users in groups based on how they consume the energy.

机译：随着对电力负载的分析正在达到从低压配电网络测得的数据，需要电网运营和管理中涉及的主要代理商根据最终用户的日常能源消耗形状对其进行细分或负载配置文件，并获得可根据用户的能源消耗对用户进行分组的模式。

著录项

来源
《Electric power systems research》 |2016年第11期|517-526|共10页
作者
Benitez Ignacio; Diez Jose-Luis; Quijano Alfredo; Delgado Ignacio;
展开▼
作者单位

Inst Tecnol Energia, Avda Juan de la Cierva 24, Paterna 46980, Spain;

Univ Politecn Valencia, Dept Syst Engn & Control, Camino Vera 14, E-46022 Valencia, Spain;

Inst Tecnol Energia, Avda Juan de la Cierva 24, Paterna 46980, Spain;

Inst Tecnol Energia, Avda Juan de la Cierva 24, Paterna 46980, Spain;

展开▼
收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Dynamic clustering; Data mining; Load profiles;

机译：动态聚类;数据挖掘;负载配置文件;

相似文献

外文文献
中文文献
专利

1. Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption [J] . Laurinec Peter, Loderer Marek, Lucka Maria, Journal of Intelligent Information Systems . 2019,第2期

机译：基于密度的无监督整体学习方法，用于时间序列预测聚集或聚集电力消耗
2. Fuzzy clustering of time series data using dynamic time warping distance [J] . Hesam Izakian, Witold Pedrycz, Iqbal Jamal Engineering Applications of Artificial Intelligence . 2015,第mara期

机译：使用动态时间规整距离对时间序列数据进行模糊聚类
3. Building electricity consumption: Data analytics of building operations with classical time series decomposition and case based subsetting [J] . Pickering Ethan M., Hossain Mohammad A., French Roger H., Energy and Buildings . 2018,第OCTa期

机译：建筑用电量：具有经典时间序列分解和基于案例的子集的建筑运营数据分析
4. Scenario analysis of city residential electricity consumption based on the time-series forecast - Taking Quanzhou city as an example [C] . Xiangchao Hou, Lujie Zhu International Conference on Civil Engineering, Architecture and Building Materials . 2012

机译：基于时间系列预测的城市住宅用电力消耗的情景分析 - 以泉州市为例
5. Distance-Based Methods for Classification and Clustering of Time Series [D] . Caiado, Aníbal Jorge da Costa Cristóvão. 2006

机译：基于距离的时间序列分类和聚类方法
6. Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption [O] . Krzysztof Gajowniczek, Marcin Bator, Tomasz Ząbkowski 2020

机译：整个时间序列数据流聚类：电力消耗的动态分析
7. Dynamic clustering of residential electricity consumption time series data based on Hausdorff distance [O] . Ignacio Benítez, José-Luis Díez, Alfredo Quijano, 2016

机译：基于Hausdorff距离的住宅电力消耗时间序列数据的动态聚类

Dynamic clustering of residential electricity consumption time series data based on Hausdorff distance

摘要

著录项

相似文献

相关主题

期刊订阅