期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2018
卷号：199
期号：5
页码：052015
DOI：10.1088/1755-1315/199/5/052015
语种：English
出版社：IOP Publishing
摘要：With the increasing scale of data in smart distribution network, the information contained in massive historical data is richer, and more valuable information can be mined. Based on the demand of electrical load analysis, the relationship between load and various potential factors in nature or society is analyzed from multiple dimensions such as time, space and meteorology, and then quickly matches the important factors. On the basis of matching the influencing factors, the intensity of association between factors and load is deeply excavated. And a more refined model, such as load-temperature model, is established to analyze the law of historical data change and predict the trend of future change, so as to support the advanced application of load forecasting.
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