电力建设2016，Vol.37Issue(12)：24-31,8.DOI:10.3969/j.issn.1000-7229.2016.12.003
联合采用熵权和灰色系统理论的电力大数据质量综合评估
Comprehensive Evaluation of Big Data Quality in Power Systems with Entropy Weight and Grey System Theory
摘要
Abstract
With the continuous expansion of power systems, as well as ever-developing technology and reduced costs of measurement devices, the recorded data in power systems have been increasing significantly and progressively exhibit the feature of big data. Much attention has been paid to the full use of big data for improving the planning, operation and control of power system, and hence how to evaluate the quality of big data is becoming an important problem to be examined. Some research publications are available on data quality improvement, such as data cleaning, data integration, and the detection of similar records, but the existing research work is still preliminary in data quality evaluations. Given this background, considering the characteristics of power systems and associated big data, this paper proposes a comprehensive method for evaluating the quality of big data in power systems. Firstly, we construct an index system for big data quality evaluations. Then aiming at the timeliness of big data, we adopt the K-means clustering algorithm in parallel with MapReduce for fast preprocessing of the big data sample set. Secondly, we use entropy weight method to calculate the objective weight of each dataset and grey evaluation method to determine the data quality level. On this basis, the comprehensive evaluation of the sample data set is carried out. Finally, the recorded electric load historical data in a city power company are employed to demonstrate the proposed method.关键词
电力系统/大数据/数据质量/评估指标/K-means聚类/熵权法/灰色理论Key words
power system/big data/data quality/evaluation index/K-means clustering/entropy weight method/grey theory分类
信息技术与安全科学引用本文复制引用
李刚,焦亚菲,刘福炎,俞敏,宋雨,文福拴..联合采用熵权和灰色系统理论的电力大数据质量综合评估[J].电力建设,2016,37(12):24-31,8.基金项目
国家自然科学基金项目(51407076)；中央高校基本科研业务费专项资金资助项目(2015ZD28)；河北省自然科学基金项目(F2014502050)；河北省高等学校科学研究项目(Z2013007)；国网浙江省电力公司经济技术研究院研究项目(JY02201403) Project supported by National Natural Science Foundation of China (51407076)；Fundamental Research Funds for the Central Universities (2015ZD28) （51407076）
