Wind Power Generation Prediction for a 5Kw Wind turbine

Renewable energies such as wind energy and solar energy are the most attractive sources of energy in the world. Researchers may need to prepare an inventory on the availability of wind energy in an area where there is no measured wind power data. In this study, air temperature, relative humidity, and wind speed data for a period of 10 years (2001– 2011) for Bandar-Abass city, were used to predict wind power generation by a 5 Kw wind turbine using ANNs. The measured data between 2001 and 2009 were used as training data set and the remained data (i.e. 2010 and 2011) used as testing dataset. In order to determine the optimal network architecture, various network architectures were designed; different training algorithms were used; the number of neuron and hidden layer and transfer functions in the hidden layer/output layer were changed. Eventually, logistic sigmoid transfer function for both hidden layers, linear transfer function for output layer and LM training algorithm were found to have a good performance.

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