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

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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).

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Authors and Affiliations

  1. EE Department, Faculty of Engineering, Islamic Azad University, South Tehran Branch, P.O. Box: 11365-4435, Tehran, Iran

    Mansour Sheikhan & Najmeh Mohammadi

  2. Research Center of West Tehran Province Power Distribution Company, Tehran, Iran

    Najmeh Mohammadi

Authors
  1. Mansour Sheikhan
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  2. Najmeh Mohammadi
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Correspondence to Mansour Sheikhan.

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Sheikhan, M., Mohammadi, N. Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection. Neural Comput & Applic 21, 1961–1970 (2012). https://doi.org/10.1007/s00521-011-0599-1

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  • DOI: https://doi.org/10.1007/s00521-011-0599-1

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