M Jabbari ghadi - University of Guilan
S Hakimi gilani
H Afrakhte
A Baghramian

Utilization of wind power as renewable resources of energy has been growing quicklyall over the world in the last decades. Wind power generation is significantly vacillating due to the wind speed alteration. Therefore, the assessment of the output power of this type of generators is always associated with some amount of uncertainties. A precise wind power prediction can efficiently uphold transmission and distribution systemoperators to improve power network control and management. This paper presents a new Imperialistic Competitive Algorithm- Neural Network (ICA-NN) method to enhance the short term wind power forecastingexactness at wind farm utilizingdata from measured information from online SCADA as well as NumericalWeather Prediction (NWP). In this method, first, a prediction model of wind speed is built based on Multilayer Perceptron MLP) artificial neural network considering environmental factors (i.e. Humidity, wind speed, temperature, geographical conditions and other factors) then, Imperialist Competitive Algorithm is used to update the neural network weights. The proposed method hasabilityof dealing with jumpingdata; and is suitable in each ofwind power and wind speed foreseeing.

Imperialistic competitive algorithm-Neural network; Numerical Weather Predictions; wind farm; Short term wind power prediction