Development of a Power Output Forecasting Tool for Wind Farms Based in Principal Components and Artificial Neural Networks

The main objective of the study here presented consists in developing a mathematical forecasting model of the available wind power output for an eight-hour horizon in wind farms that may be affected by inclement meteorological environments where the surface of the wind turbine blades can suffer of ice accumulation. These events may depend on several factors as air temperature, relative humidity, barometric pressure or wind speed, among others. In this way a precise model depending on the referred variables will allow predicting with higher accuracy the available power at the plant when the referred events may occur. A model based in neural networks for the prediction of the available power output of an experimental wind farm has been developed and tested using real data. The proposed model outperforms other professional commercial models.