Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting

Rahmani, Rasoul, Yusof, Rubiyah, Seyedmahmoudian, Mehdi and Mekhilef, Saad 2013, Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting, Journal of wind engineering and industrial aerodynamics, vol. 123, no. A, pp. 163-170, doi: 10.1016/j.jweia.2013.10.004.

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Title Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting
Author(s) Rahmani, Rasoul
Yusof, Rubiyah
Seyedmahmoudian, Mehdi
Mekhilef, Saad
Journal name Journal of wind engineering and industrial aerodynamics
Volume number 123
Issue number A
Start page 163
End page 170
Total pages 8
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2013-12
ISSN 0167-6105
Keyword(s) Wind energy
Short term forecasting
Hybrid technique
Ant Colony Optimization
Particle Swarm Optimization
Summary Wind farms are producing a considerable portion of the world renewable energy. Since the output power of any wind farm is highly dependent on the wind speed, the power extracted from a wind park is not always a constant value. In order to have a non-disruptive supply of electricity, it is important to have a good scheduling and forecasting system for the energy output of any wind park. In this paper, a new hybrid swarm technique (HAP) is used to forecast the energy output of a real wind farm located in Binaloud, Iran. The technique consists of the hybridization of the ant colony optimization (ACO) and particle swarm optimization (PSO) which are two meta-heuristic techniques under the category of swarm intelligence. The hybridization of the two algorithms to optimize the forecasting model leads to a higher quality result with a faster convergence profile. The empirical hourly wind power output of Binaloud Wind Farm for 364 days is collected and used to train and test the prepared model. The meteorological data consisting of wind speed and ambient temperature is used as the inputs to the mathematical model. The results indicate that the proposed technique can estimate the output wind power based on the wind speed and the ambient temperature with an MAPE of 3.513%.
Language eng
DOI 10.1016/j.jweia.2013.10.004
Field of Research 090602 Control Systems, Robotics and Automation
090608 Renewable Power and Energy Systems Engineering (excl Solar Cells)
0905 Civil Engineering
0913 Mechanical Engineering
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2013, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30080853

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