DocumentCode :
2987089
Title :
Application of data mining techniques to load profiling
Author :
Pitt, B.D. ; Kitschen, D.S.
Author_Institution :
Univ. of Manchester Inst. of Sci. & Technol., UK
fYear :
1999
fDate :
36342
Firstpage :
131
Lastpage :
136
Abstract :
In the UK supply market, customers can purchase electricity from any supplier regardless of size and location. Accordingly there is special interest in understanding the nature of variations in load shape, to devise better competitive tariff structures and facilitate aggressive niche marketing. Utilities have databases of half-hourly loads too large to be interpreted by hand and eye; potentially valuable information is hidden therein which is not revealed by coarse statistics. The heterogeneity of response, the large number of predictors, and the sheer size of these databases impose severe theoretical and computational difficulties on load shape modeling. Data mining refers (in part) to the use of adaptive nonparametric models (which vary their strategy according to the local nature of the data) for efficiently discovering knowledge in just such databases. A method centering on adaptive decision tree clustering of load profiles is presented, and results utilising an actual database are discussed
Keywords :
data mining; database management systems; decision trees; electricity supply industry; load (electric); power system analysis computing; UK supply market; adaptive decision tree clustering; adaptive nonparametric models; competitive tariff structures; data mining techniques; electricity purchase; half-hourly loads databases; knowledge discovery; load profiling; load shape variations; niche marketing; Clustering methods; Costs; Data mining; Databases; Decision trees; Load modeling; Predictive models; Shape; Statistics; Weather forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Industry Computer Applications, 1999. PICA '99. Proceedings of the 21st 1999 IEEE International Conference
Conference_Location :
Santa Clara, CA
Print_ISBN :
0-7803-5478-8
Type :
conf
DOI :
10.1109/PICA.1999.779395
Filename :
779395
Link To Document :
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