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Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting
Abstract 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 364days 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%.
Highlights The method is used to forecast the wind energy output of a real wind farm. The output power data is collected from Binaloud Wind Farm which consists of 43 wind turbines of 660kW and yields a maximum capacity of 28.4MW. The environmental data which contains wind speed profile and ambient temperature is taken from the Meteorological Office of Nishabour. The hourly data from April 2010 until January 2011 is used for training the model while the remaining data of February and March 2011 is used for testing. The hybrid method performs better than the individual PSO and ACO with a mean absolute percentage error of 3.53%.
Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting
Abstract 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 364days 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%.
Highlights The method is used to forecast the wind energy output of a real wind farm. The output power data is collected from Binaloud Wind Farm which consists of 43 wind turbines of 660kW and yields a maximum capacity of 28.4MW. The environmental data which contains wind speed profile and ambient temperature is taken from the Meteorological Office of Nishabour. The hourly data from April 2010 until January 2011 is used for training the model while the remaining data of February and March 2011 is used for testing. The hybrid method performs better than the individual PSO and ACO with a mean absolute percentage error of 3.53%.
Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting
Rahmani, Rasoul (author) / Yusof, Rubiyah (author) / Seyedmahmoudian, Mohammadmehdi (author) / Mekhilef, Saad (author)
Journal of Wind Engineering and Industrial Aerodynamics ; 123 ; 163-170
2013-10-05
8 pages
Article (Journal)
Electronic Resource
English