Soft computing applications in wind speed and power prediction for wind energy

This paper proposes the prediction schemes for Wind Speed and Power using soft computing techniques. These techniques construct prediction system from input-output model directly without using any earlier information. The soft computing techniques, do not model the system; instead, they find an appropriate mapping between the several inputs and the wind speed and power, usually learned from historical examples, thus being computationally more efficient. In this paper various soft computing based techniques like Fuzzy Predictor, Adaptive Neural Fuzzy Inference System and Committee Machines has been presented. We evaluate the accuracy of wind speed and power prediction attained with the proposed neural network predictor, reporting the results of mainland of Prince Edward Island, Canada.