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首页> 外文期刊>Applied Energy >Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data
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Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data

机译:使用大量特定于客户的按小时测量的用电数据来创建用电负荷概况的基于数据的方法

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摘要

The recent technological developments monitoring the electricity use of small customers provides with a whole new view to develop electricity distribution systems, customer-specific services and to increase energy efficiency. The analysis of customer load profile and load estimation is an important and popular area of electricity distribution technology and management. In this paper, we present an efficient methodology, based on self-organizing maps (SOM) and clustering methods (K-means and hierarchical clustering), capable of handling large amounts of time-series data in the context of electricity load management research. The proposed methodology was applied on a dataset consisting of hourly measured electricity use data, for 3989 small customers located in Northern-Savo, Finland. Information for the hourly electricity use, for a large numbers of small customers, has been made available only recently. Therefore, this paper presents the first results of making use of these data. The individual customers were classified into user groups based on their electricity use profile. On this basis, new, data-based load curves were calculated for each of these user groups. The new user groups as well as the new-estimated load curves were compared with the existing ones, which were calculated by the electricity company, on the basis of a customer classification scheme and their annual demand for electricity. The index of agreement statistics were used to quantify the agreement between the estimated and observed electricity use. The results indicate that there is a clear improvement when using data-based estimations, while the new-estimated load curves can be utilized directly by existing electricity power systems for more accurate load estimates.
机译:监视小客户用电的最新技术发展为开发配电系统,针对客户的服务和提高能源效率提供了全新的视角。客户负载分布图和负载估计的分析是配电技术和管理的重要且受欢迎的领域。在本文中,我们提出了一种基于自组织映射(SOM)和聚类方法(K均值和层次聚类)的有效方法,能够在电力负荷管理研究的背景下处理大量时间序列数据。针对芬兰北部萨沃省的3989个小客户,将拟议的方法应用于由小时测量的用电量数据组成的数据集。仅在最近才为大量小客户提供每小时用电量的信息。因此,本文提出了利用这些数据的初步结果。根据用户的用电情况将其分为用户组。在此基础上,为这些用户组中的每一个计算了新的基于数据的负载曲线。根据用户分类方案及其年用电量,将新用户组以及新估算的负荷曲线与电力公司计算出的现有负荷曲线进行比较。协议统计指标用于量化估计和观察到的用电量之间的一致性。结果表明,当使用基于数据的估计时,有了明显的改进,而新估计的负载曲线可以直接由现有的电力系统利用,以进行更准确的负载估计。

著录项

  • 来源
    《Applied Energy》 |2010年第11期|P.3538-3545|共8页
  • 作者单位

    Department of Environmental Sciences, University of Eastern Finland P.O. Box 1627, FIN-70211 Kuopio, Finland;

    Department of Mechanical Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece;

    rnDepartment of Environmental Sciences, University of Eastern Finland P.O. Box 1627, FIN-70211 Kuopio, Finland;

    Department of Mechanical Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece;

    rnDepartment of Environmental Sciences, University of Eastern Finland P.O. Box 1627, FIN-70211 Kuopio, Finland;

  • 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

    electricity use; load curves; load profiling; time-series clustering; self-organizing map; energy efficiency;

    机译:用电;负荷曲线;负载分析;时间序列聚类;自组织图;能源效率;

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