基于大数据的电力生产风险评估模型及实验分析

Wang Zeyong, Hong Yutian, Tong Zhongzheng
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引用次数: 1

摘要

本文研究电力大数据的特点,针对电力系统面临的数据质量问题。提出了一种电力系统数据质量的评价方法。根据电力大数据的特点,对影响数据的一系列指标进行分析和分层划分,确定电力生产数据在风险管理过程中的计量标准,即风险指标体系。然后,借鉴其他领域的风险评估模型或数据挖掘中的演绎和归纳规则,建立电力数据风险评估模型。它可以用于电力系统数据质量的评估,找到适合于大数据质量评估的框架和解决方案。最后,在Hadoop平台上对该模型进行了实现,证明该模型兼顾了指标体系的完备性、评价方法的客观性和计算方法的快速性。
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Risk Assessment Model and Experimental Analysis of Electric Power Production Based on Big Data
This paper studies the characteristics of big data of power, and aims at the data quality problems faced by power system. It puts forward an assessment method of power system data quality. Based on the characteristics of large power data, a series of indicators influencing the data are analyzed and hierarchically divided to determine the measurement standard of power production data during the process of risk management, namely, the risk index system. Then, the risk assessment model of power data is established by referring to the assessment model in other fields or the rules of deduction and induction in data mining. It can be used to evaluate the quality of power system data, and find a framework and solution suitable for large data quality assessment. Finally, the model is implemented on Hadoop platform, which proves that it takes into account the completeness of the index system, the objectivity of the assessment method and the rapidity of the calculation method.
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