中国电机工程学报Issue(1)：43-51,9.DOI:10.13334/j.0258-8013.pcsee.2015.01.006
考虑气温因素的负荷特性统计指标关联特征数据挖掘
Data Mining on Correlation Feature of Load Characteristics Statistical Indexes Considering Temperature
摘要
Abstract
With the big data of load characteristics statistical indexes and temperature indexes increasing, it is significant to obtain correlative features of electric data effectively for planning and operation in power system. In this paper, a approach which can extract the temperature influence on load characteristics indexes and the internal correlation features was proposed. Considering temperature and load seasonal characteristics, this paper conducted modeling analysis in each season respectively. First, the qualitative analyses of potential physical relations among the indexes as well as quantitative calculation via Pearson correlation coefficient of historical data were coordinated to draw the correlation features between two factors. Then, based on the stationary test results on the origin series, long-term synchronous movement trend and short-term fluctuant characteristics were obtained via co-integration test on the difference sequence of non-stationary index and vector error correction (VEC) model. Further, through vector auto- regression (VAR) model on stationary time series of variables after difference, the dynamic correlation of multi-variable and the causal guiding relationship among related variables were acquired integrated with Granger Causality. The statistics of load characteristics from 2006 to 2010 of a provincial power grid in Central China demonstrate the effectiveness and correctness of this method, and the method has been applied in the actual load characteristic statistical analysis of power grid.关键词
大数据/负荷特性统计指标/相关性/联动性/格兰杰因果分析Key words
big data/load indexes/correlation/linkage/Granger causality分类
信息技术与安全科学引用本文复制引用
马瑞,周谢,彭舟,刘道新,徐慧明,王军,王熙亮..考虑气温因素的负荷特性统计指标关联特征数据挖掘[J].中国电机工程学报,2015,(1):43-51,9.基金项目
国家自然科学基金项目(51277015)；国家电网公司科技项目([2012]515)。Project Supported by National Natural Science Foundation of China (51277015) （51277015）
Science and Technology Projects of State Grid ([2012]515)) （[2012]515）
