NDLI: Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection

Content Provider	Springer Nature Link
Author	Sheikhan, Mansour Mohammadi, Najmeh
Copyright Year	2011
Abstract	Due to deregulation of electricity industry, accurate load forecasting and predicting the future electricity demand play an important role in the regional and national power system strategy management. Electricity load forecasting is a challenging task because electric load has complex and nonlinear relationships with several factors. In this paper, two hybrid models are developed for short-term load forecasting (STLF). These models use “ant colony optimization (ACO)” and “combination of genetic algorithm (GA) and ACO (GA-ACO)” for feature selection and multi-layer perceptron (MLP) for hourly load prediction. Weather and climatic conditions, month, season, day of the week, and time of the day are considered as load-influencing factors in this study. Using load time-series of a regional power system, the performance of ACO + MLP and GA-ACO + MLP hybrid models is compared with principal component analysis (PCA) + MLP hybrid model and also with the case of no-feature selection (NFS) when using MLP and radial basis function (RBF) neural models. Experimental results and the performance comparison with similar recent researches in this field show that the proposed GA-ACO + MLP hybrid model performs better in load prediction of 24-h ahead in terms of mean absolute percentage error (MAPE).
Starting Page	1961
Ending Page	1970
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	8
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-05-01
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Short-term load forecasting Feature selection Ant colony optimization Genetic algorithm Neural network Computational Biology/Bioinformatics Computational Science and Engineering Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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