Feature selection for daily peak load forecasting using a neuro-fuzzy system

Abstract

Accurate electrical daily peak load forecasting (DPLF) is essential for power system management in order to prevent overloading and grid failure. Fuzzy neural networks have been successfully applied to load forecasting due to their nonlinear mapping and generalized behavior. In this paper, a neuro-fuzzy based DPLF (N-DPLF) model with a feature selection method is proposed for DPLF. The load data is clustered into seven subsets according to the season and day type. For each subset, the four features with the highest salience ranks are selected. After training N-DPLF model, the formed BSWs (bounded sum of weighted fuzzy membership functions) in accordance with the selected features denote characteristics of these features. The N-DPLF model provides explicit BSWs in hyperboxes, instead of the uncertain black box nature of neural network models, so that the selected features can be interpreted by the visually constructed BSWs. The N-DPLF model with a feature selection method shows a mean absolute percentage error (MAPE) of 1.86 % using Korea Power Exchange data over 1-year period.