Publication: Spatial Pattern Recognition of Urban Sprawl Using a Geographically Weighted Regression for Spatial Electric Load Forecasting
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Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Ieee
Type
Work presented at event
Access right
Acesso aberto
Abstract
Distribution utilities must perform forecasts in spatial manner to determine the locations that could increase their electric demand. In general, these forecasts are made in the urban area, without regard to the preferences of the inhabitants to develop its activities outside the city boundary. This may lead to errors in decision making of the distribution network expansion planning. In order to identify such preferences, this paper presents a geographically weighted regression that explore spatial patterns to determines the probability of rural regions become urban zones, as part of the urban sprawl. The proposed method is applied in a Brazilian midsize city, showing that the use of the calculated probabilities decreases the global error of spatial load forecasting in 6.5% of the load growth.
Description
Keywords
Distribution network planning, geographically weighted regression, spatial regression, spatial electric load forecasting
Language
English
Citation
2015 18th International Conference On Intelligent System Application To Power Systems (isap). New York: Ieee, 5 p., 2015.
