Abstract
In recent years, the small-scale wind power generation increases rapidly worldwide. The wind power depends on the wind speed, which is a random variable and is irregular. For efficient operation of wind power plants accurate short-term forecasts are essential. The knowledge of future power generation from wind turbines is useful for schedulers, transmission operators and energy traders. In this paper, wind speed and the power generation are predicted using a particle swarm optimization (PSO) algorithm -Neural hybrid system. The neural network is used in many prediction systems and it gives very successful results compared to other forecasting techniques like persistence, mean methods etc. At present many prediction systems are built with ANFIS but it is a more complex structure and not suitable for small scale wind power plants. The result of this hybrid system shows that it is more accurate and reliable for short term wind power forecasting. In this paper we have compared the results of hybrid model with the earlier available techniques like standard neural network and Genetic Neural Network for verification purpose.
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Pratheepraj, E., Abraham, A., Deepa, S.N., Yuvaraj, V. (2011). Very Short Term Wind Power Forecasting Using PSO-Neural Network Hybrid System. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22720-2_53
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DOI: https://doi.org/10.1007/978-3-642-22720-2_53
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