Saudi Arabia is a large country with diverse geography and climates. That diversity gives it strong potential for many types of renewable energy—not just solar. While solar often takes the spotlight, wind energy also shows promise in many parts of the Kingdom. The main challenge is finding the best sites for new wind farms. Factors like wind speed, direction, terrain elevation, distance to the power grid, climate, and humidity all affect how well a site will perform.
This is where AI could shine. AI systems can analyze huge amounts of data—such as ground measurements, aerial surveys, meteorological reports, and topographical maps—to create insightful dashboards, forecasts, and suitability maps. The more high-quality data these systems have, over longer time periods, the more accurate their predictions can become.
AI Studies on Wind Farm Site Selection
In the study Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia (Tayeb Brahimi et al., Energies, 2019), researchers tested several AI and machine learning models. These included Artificial Neural Networks (ANN), Support Vector Machines (SVM), Fuzzy Logic, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS).
They trained and tested the models using meteorological datasets from King Abdullah City for Atomic and Renewable Energy (K.A.CARE). The results showed that ANN models gave the best balance of performance and simplicity. They produced low root mean square error (RMSE) values and high correlation coefficients (R) between predicted and actual wind speeds. This confirmed that, with good local data, ANN models can accurately forecast wind speeds in Saudi locations that have varied climates and terrain.
AI Tools Already in Use
Some platforms have already started using AI and machine learning for renewable energy planning:
- Microsoft AI for Earth Wind Forecasting Models: This platform uses neural networks trained on satellite and weather datasets to forecast wind speeds and energy output. It has been used by renewable energy developers in the United States and India to plan future wind projects.
- IBM Watson Machine Learning with The Weather Company Renewable Energy Forecasting: This solution uses machine learning on historical weather and turbine data to predict wind power output up to 15 days ahead. Utilities in Europe and North America use it to support grid operations and site planning.
These examples show how AI can turn complex, scattered data into clear insights, something traditional platforms alone cannot do.
Can AI really decide?
Going back to our first question—can AI really decide? Not entirely on its own, at least not yet. But AI can be a game-changer in wind farm planning. It can analyze complex datasets, generate forecasts, map suitability, and help optimize turbine layouts faster and more accurately than traditional methods. While human oversight is still needed for policy, environmental, and economic decisions, AI gives us the tools to make smarter, more efficient, and more sustainable choices.
The future of renewable energy depends on embracing these technologies. By investing in high-quality data and AI-driven models, engineers and planners can unlock the full potential of wind resources. Now is the time to act, innovate, and lead the way toward a cleaner, brighter energy future.