An hybrid aggregate model applied to the short-term bus load forecasting problem

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.