Neural networks can improve wind farm forecasts
Published Thursday, 30th April 2009
Researchers from the University of Alcala and the Complutense University in Madrid have invented a new method for predicting the wind speed of wind farm aerogenerators … using knowledge generated from systems that simulate the workings of animals’ nervous systems.
A combination of weather forecasting models and artificial neural networks, the new method enables researchers to calculate the energy that wind farms will produce two days in advance.
“The aim of the hybrid method we have developed is to predict the wind speed in each of the aerogenerators in a wind farm,” said Sancho Salcedo, an engineer at the Escuela Politécnica Superior.
The research team developed the model using information provided by the Global Forecasting System from the US National Centres 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.
The researchers then refined their predictions using a model from the US National Center of Atmospheric Research that’s designed to enhance resolution to 15×15 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 said.
Such networks are automatic information learning and processing systems that simulate the workings of animal nervous systems. Instead of inputting biological data, however, the researchers fed the networks data on temperature, atmospheric pressure and wind speed provided by forecasting models, as well as by the aerogenerators themselves.
With these data, once the system has been “trained,” predictions regarding wind speed can 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,” he said. “Therefore, by summing the predictions for each ‘aero,’ we can forecast the production of an entire wind farm.”
The method has already been successfully tested at a wind farm in Fuentasanta, Spain.
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