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Check average wind speed at any location on Earth. Select your hub height, view monthly profiles, and assess wind potential — all for free.
Wind speed increases with altitude due to reduced friction from the Earth's surface. This phenomenon, called wind shear, is quantified by the shear exponent (alpha) in the power law equation: V(h) = V(ref) × (h / h_ref)^alpha. The value of alpha depends heavily on terrain roughness — smooth surfaces like open water have low shear (0.10-0.12), while urban areas can have shear exponents exceeding 0.35.
For wind energy projects, hub heights typically range from 80m to 150m. Even small increases in hub height can significantly boost energy capture — since wind power is proportional to the cube of wind speed, a 10% increase in wind speed translates to approximately 33% more available power.
This tool calculates a site-specific shear exponent from the ratio of observed wind speeds at 10m and 100m, then extrapolates to your chosen hub height. The terrain selector provides a physical lower bound to prevent unrealistically low shear values.
The International Electrotechnical Commission (IEC) classifies sites into wind classes to help match turbine designs to site conditions. Higher wind classes require turbines built to withstand stronger loads.
| IEC Class | Mean Speed Range | Typical Sites | Turbine Design |
|---|---|---|---|
| I (High) | > 10 m/s | Coastal ridgelines, offshore, mountain passes | Strongest, shortest blades |
| II (Medium) | 8.5 – 10 m/s | Open plains, moderate coastal | Standard design loads |
| III (Low) | 7.5 – 8.5 m/s | Agricultural land, inland hills | Longer blades, taller towers |
| IV (Very Low) | 6.0 – 7.5 m/s | Forested areas, moderate terrain | Low-wind specialist turbines |
| S (Below standard) | < 6.0 m/s | Urban, heavily forested | Non-standard / small wind |
Wind speed is the most critical input for estimating energy production — but it is only the starting point. Converting wind speed to actual energy output requires a turbine power curve, which maps wind speed to electrical output at each hour. Factors like turbulence intensity, wake losses, and availability further reduce real-world production compared to gross estimates.
WindAI's full assessment uses machine learning trained on 300+ real wind farms to predict hourly capacity factors, annual energy production (AEP), and P50/P90 exceedance statistics — far more accurate than simple speed-based estimates.