能源消耗统计分析中错误数据的发现与解释

I. Makarova, A. M. Ignatenko, A. Kopyrin
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引用次数: 0

摘要

对各种情况下的能源消耗进行监测和分析,并及时测量参数(指标),对现代经济至关重要。这项工作致力于检查和解释在市政组织中收集能源消耗数据(以天然气消耗为例)的异常情况。天然气消费对城市的社会经济领域很重要。非法连接是造成非技术性资源浪费的主要原因。传统的漏气检测方法效率低,耗时长。现代数据分析技术将允许检测和解释消费的异常情况,并形成列表以检查对象是否存在未经授权的连接。作者的特殊贡献在于应用了一套统计方法,旨在处理和识别城市结构的能源消耗异常。值得注意的是,使用这些技术需要开发有效的算法,并实现自动化和机器学习算法。时间序列数据的新视角有助于识别异常,优化决策等。这些过程可以自动化。所提出的方法在描述气体消耗的时间序列数据上进行了测试,可用于更广泛的任务。该研究可以与知识发现方法和深度学习算法相结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection and interpretation of erroneous data in statistical analysis of consumption of energy resources
Monitoring and analysis of consumption of energy resources in various contexts, as well as measuring of parameters (indicators) in time are of utmost importance for the modern economy. This work is dedicated to examination and interpretation of the anomalies of collecting data on consumption of energy resources (on the example of gas consumption) in the municipal formation. Gas consumption is important for the socioeconomic sphere of cities. Unauthorized connections are the key reason for non-technological waste of the resource. The traditional methods of detection of stealing of gas are ineffective and time-consuming. The modern technologies of data analysis would allow detecting and interpreting the anomalies of consumption, as well as forming the lists for checking the objects for unauthorized connections. The author’s special contribution lies in application of the set of statistical methods aimed at processing and identification of anomalies in energy consumption of a municipal formation. It is worth noting that the use of such technologies requires the development of effective algorithms and implementation of automation and machine learning algorithms. The new perspective upon time-series data facilitates identification of anomalies, optimization of decision-making, etc. These processes can be automated. The presented methodology tested on time-series data that describes the consumption of gas can be used for a broader range of tasks. The research can be combined with the methods of knowledge discovery and deep learning algorithms.
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