Title :
An hybrid aggregate model applied to the short-term bus load forecasting problem
Author :
Salgado, Ricardo Menezes ; Ballini, Rosangela ; Ohishi, Takaaki
Author_Institution :
Dept. of Exact Sci., Fed. Univ. of Alfenas, Alfenas, Brazil
Abstract :
In this paper we present a hybrid methodology built on a combination of clustering and forecasting techniques used to solve the short-term bus load forecasting problem. The proposed method was made in two phases: In the first phase a clustering algorithm is used to identify buses clusters with similar daily load profile and in the second phase is proposed an aggregate structure for to foresee each bus using a conventional prediction model. The methodology was applied on bus load data from the Brazilian North/Northeast system and the results showed that the model was efficient with 2% to 3.6% of the mean percentage error level on the buses.
Keywords :
load forecasting; power system simulation; statistical analysis; Brazilian Northeast system; bus load data; buses clusters; clustering algorithm; conventional prediction model; hybrid aggregate model; load profile; mean percentage error; short-term bus load forecasting; Aggregates; Artificial neural networks; Clustering algorithms; Economic forecasting; Load forecasting; Power generation; Power system modeling; Power system reliability; Predictive models; State estimation; Aggregate Forecasting Model; Artificial Neural Networks; Clustering Algorithm; Multiple Linear Regression; Short-Term Bus Load Forecasting; Time Series Analysis;
Conference_Titel :
PowerTech, 2009 IEEE Bucharest
Conference_Location :
Bucharest
Print_ISBN :
978-1-4244-2234-0
Electronic_ISBN :
978-1-4244-2235-7
DOI :
10.1109/PTC.2009.5282153
