Paper Info
Title
Feature selection for daily peak load forecasting using a neuro-fuzzy system
Abstract
urate 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.
Year
DOI
Venue
2015
10.1007/s11042-014-1943-0
Multimedia Tools and Applications
Keywords
Field
DocType
Daily peak load forecasting,Feature selection,Weighted fuzzy membership function
Black box (phreaking),Mean absolute percentage error,Data mining,Neuro-fuzzy,Feature selection,Pattern recognition,Computer science,Fuzzy logic,Electric power system,Artificial intelligence,Artificial neural network,Grid
Journal
Volume
Issue
ISSN
74
7
1380-7501
Citations 
PageRank 
References 
3
0.41
15
Authors
4
Name
Order
Citations
PageRank
Sung-Yong Son130.41
Sang-Hong Lee2293.12
Kyung-Yong Chung3115480.87
Joon Shik Lim4516.39