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Spatiotemporal Data Mining for Distribution Load ExpansionARANGO, H. G. , LAMBERT-TORRES, G. , de MORAES, C. H. V. , BORGES DA SILVA, L. E.
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data mining, intelligent systems, load modeling, power distribution, urban areas
urban(9), power(9), economic(7), torres(6), systems(6), silva(6), economics(6), data(6), spatial(5), mining(5)
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About this article
Date of Publication: 2016-08-31
Volume 16, Issue 3, Year 2016, On page(s): 65 - 72
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.03010
Web of Science Accession Number: 000384750000010
SCOPUS ID: 84991087193
The load spatial forecasting is fundamental for the electric energy distribution systems planning. Several methods using different conceptions have been proposed to determine the future configuration of the electric markets. This paper proposes a dynamic model of load expansion, based on concepts of local analysis using ideas and applications from urban poles theory. Thus, the load expansion is simulated in a dynamic way, maintaining a continuous change in the conditions for localization of a new load unit. An algorithm generating a snapshot that represents the distribution system configuration at that instant determines the geometry of the market in a given instant. The proposed dynamic model, based on the urban poles theory, has the capacity for summing up the information from economic variables sets, expressed in terms of interchange flow laws, which are modeled by distance and transportation functions. This supplies the model with the capacity for being used even though the number of available explanatory variables is reduced.
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 Modified imperialist competitive optimization to high resolution spatial electric load demand forecasting, Grilo, Marcel Mendonça, de Moraes, Carlos Henrique Valério, Costa, Claudio Inácio de Almeida, Lambert-Torres, Germano, Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Issue 5, Volume 35, 2018.
Digital Object Identifier: 10.3233/JIFS-171971 [CrossRef]
 A Behavioral Economics Approach to Residential Electricity Consumption, Siebert, Luciano, Sbicca, Adriana, Aoki, Alexandre, Lambert-Torres, Germano, Energies, ISSN 1996-1073, Issue 6, Volume 10, 2017.
Digital Object Identifier: 10.3390/en10060768 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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