AI Methods boost Wind Power Prediction Accuracy in Spain
Last year, wind energy accounted for 23.2% of all energy injected into the Spanish electricity system,according too data published by Red Eléctrica in its latest 2024 report. Although wind power leads national energy production, its dependence on weather conditions and its inherently intermittent nature present challenges.Therefore, fine-tuning wind speed prediction data for these infrastructures is a key task to optimize the management and performance of wind turbines.
This is precisely what the AYRNA group at the University of Córdoba (UCO) has proposed, using artificial intelligence to help fill the sails of wind power as it were. The team has confirmed two methodologies trained on over 13 years of data, capable of predicting extreme speeds with greater accuracy than traditional methods, using variables such as wind components at different altitudes, pressures, and air temperatures. The research is published in the journal Energy and AI.
Both systems are based on artificial neural networks inspired by the human b
AI improves wind speed prediction in wind farms, boosting efficiency
Researchers at the University of Córdoba have developed a new artificial intelligence (AI) model that significantly improves the prediction of wind speed within wind farms. This advancement promises to enhance energy production and reduce operational costs.
Traditional wind speed prediction methods often struggle with the complex and variable nature of wind flow within a wind farm, where turbines can influence each other’s performance. The new model, based on ordinal classification, focuses on predicting the ranking of wind speeds rather than the exact values. This approach proves to be more robust and accurate, especially in complex terrains.
“Instead of trying to predict the precise wind speed, which is arduous, we predict its relative position within a set of ordered categories-for example, very low, low, medium, high, very high,” explains A.M. Gómez-Orellana, lead author of the study published in Energy and AI. “This simplifies the problem and makes the model more reliable.”
The researchers trained and tested their model using real-world data from a wind farm, demonstrating its superior performance compared to conventional methods. Improved wind speed prediction allows for more accurate adjustments to turbine operation, maximizing energy capture and minimizing stress on the equipment. This translates to increased efficiency, lower maintainance costs, and a more stable energy supply.
The team believes this technology has the potential to be widely adopted in the wind energy industry, contributing to a more enduring and efficient energy future.
More details:
A.M. Gómez-Orellana et al, Enhancing wind speed prediction in wind farms through ordinal classification, energy and AI (2025). DOI: 10.1016/J.EGYAI.2025.100596
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University of Córdoba