Aggregated functional data model applied on clustering and disaggregation of UK electrical load profiles

被引:3
作者
Franco, Gabriel [1 ,3 ]
de Souza, Camila P. E. [2 ]
Garcia, Nancy L. [1 ]
机构
[1] Univ Estadual Campinas UNICAMP, Dept Stat, Sao Paulo, Brazil
[2] Univ Western Ontario, Dept Stat & Actuarial Sci, London, ON, Canada
[3] Univ Estadual Campinas UNICAMP, Dept Stat, Rua Sergio Buarque Holanda 651, Campinas, SP, Brazil
基金
巴西圣保罗研究基金会; 加拿大自然科学与工程研究理事会;
关键词
aggregated functional data model; basis function expansion; clustering; electrical load profiles; functional data analysis; Gaussian process; BASIS SELECTION; MAXIMUM-LIKELIHOOD; SMART METERS; REGRESSION; MIXTURES; PREDICTION;
D O I
10.1093/jrsssc/qlac006
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Understanding electrical energy demand at the consumer level plays an important role in planning the distribution of electrical networks and offering of off-peak tariffs, but observing individual consumption patterns is still expensive. On the other hand, aggregated load curves are normally available at the substation level. The proposed methodology separates substation aggregated loads into estimated mean consumption curves, called typical curves, including information given by explanatory variables. In addition, a model-based clustering approach for substations is proposed based on the similarity of their consumers' typical curves and covariance structures. The methodology is applied to a real substation load monitoring dataset from the UK and tested in eight simulated scenarios.
引用
收藏
页码:48 / 75
页数:28
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