An intelligent ACO-SA approach for short term electricity load prediction

Authors:
Arash Ghanbari;Esmaeil Hadavandi;Salman Abbasian-Naghneh
Affiliations:
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran;Department of Mathematics, Islamic Azad University, Najafabad, Iran
Venue:
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Year:
2010

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Abstract

Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. On the other hand, electrical load prediction is one of the important concerns of power systems so development of intelligent prediction tools for performing accurate predictions is essential. This study presents an intelligent hybrid approach called ACO-SA by hybridization of Ant Colony Optimization (ACO) and Simulated Annealing (SA). The hybrid approach consists of two general stages. At the first stage time series inputs will be fed into ACO and it performs a global search to find a globally optimum solution. At the second stage, ACO's outcome will be fed into SA as initial solution and then SA starts local search around the ACO's global optimum and performs the tuning process on the initial solution. The superiority and applicability of the ACO-SA approach is shown for Iranian monthly load prediction problem and outcomes of the hybrid method are compared with Single ACO, Single SA and ANN technique (which is a common technique in the field of load prediction). Results show that ACO-SA approach outperforms rest of the methods regarding to prediction accuracy, so it can be considered as a promising alternative for load prediction studies.