Neural Networks Used To Improve Wind Speed Forecasting

"The aim of the hybrid method we have developed is to predict the wind speed in each of the aerogenerators in a wind farm", explained Sancho Salcedo, an engineer at the Escuela Politécnica Superior and co-author of the study, published on-line in the journal Renewable Energy.

In order to develop the new model, the scientists used information provided by the Global Forecasting System from the US National Centers for Environmental Prediction. The data from this system cover the entire planet with a resolution of approximately 100 kilometres and are available for free on the internet.

Researchers are able to make more detailed predictions by integrating the so-called ‘fifth generation mesoscale model (MM5), from the US National Center of Atmospheric Research, designed to enhance resolution to 15x15 kilometres.

"This information is still not enough to predict the wind speed of one particular aerogenerador, which is why we applied artificial neural networks," Salcedo clarified. These networks are automatic information learning and processing systems that simulate the workings of animal nervous systems. In this case, they use the temperature, atmospheric pressure and wind speed data provided by forecasting models, as well as the data gathered by the aerogenerators themselves.

With these data, once the system has been "trained", predictions regarding wind speed will be made between one and 48 hours in advance. Wind farms are obliged by law to supply these predictions to Red Eléctrica Española, the company that delivers electricity and runs the Spanish electricity system.

Salcedo says the method can be applied immediately: "If the wind speed of one aerogenerator can be predicted, then we can estimate how much energy it will produce. Therefore, by summing the predictions for each ‘aero', we can forecast the production of an entire wind farm." The method has already been used very successfully at the wind farm in Fuentasanta, in Albacete.

Millions of Euros could be saved

Researchers are continuing to improve the method and recently proposed the use of several global forecasting models instead of just one, according to an article published this year in Neurocomputing. As a result, several sets of observations are obtained, which are then applied to banks of neural networks to achieve a more accurate prediction of aerogenerator wind speeds.

The results obtained reveal an improvement of 2% in predictions compared to the previous model. "Although this may seem like a small improvement, it is really substantial, as we are talking about an improvement in predicting energy production that could be worth millions of euros, Salcedo concluded.