Title :
A short-term bus load forecasting system
Author :
Salgado, Ricardo Menezes ; Ohishi, Takaaki ; Ballini, Rosangela
Author_Institution :
Inst. of Exact Sci., Fed. Univ. of Alfenase, Alfenas, Brazil
Abstract :
This paper proposes a methodology for a short-term bus load forecasting. This approach calculates the short-term bus load demand forecast using few aggregated models. The idea is to cluster the buses in groups with similar daily load profile and for each cluster one bus load forecasting model is adjusted. For each cluster, aggregated forecasting model is built based on the analysis of individual bus load data. The solution obtained through aggregated approach is similar to the solution obtained by individual bus load forecasting model, but requiring much less computational time. This proposed methodology was implemented in a friendly computational forecasting support system described in this paper.
Keywords :
demand forecasting; load forecasting; aggregated forecasting model; bus load demand forecast; bus load forecasting model; computational forecasting support system; computational time; daily load profile; individual bus load data; Artificial neural networks; Computational modeling; Data models; Forecasting; Load forecasting; Load modeling; Predictive models; Artificial Neural Network; Bus Load Forecasting; Clustering Algorithm; Forecasting Support System;
Conference_Titel :
Hybrid Intelligent Systems (HIS), 2010 10th International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7363-2
DOI :
10.1109/HIS.2010.5600075