Wind Energy Calculator

Annual Energy Production (AEP) is estimated by combining a wind speed probability distribution with a turbine's power curve. The calculation integrates across all possible wind speeds to determine expected energy output over a year.

This calculator uses the Rayleigh distribution, a special case of the Weibull distribution (shape factor k=2), which is widely used as a first approximation for wind speed distributions. The Rayleigh PDF is:

For each wind speed bin, we multiply the probability of that wind speed occurring by the turbine's power output at that speed, then sum across all bins and multiply by 8,760 hours in a year. The power curve uses a cubic ramp from cut-in to rated wind speed, flat output at rated power, and zero output above cut-out speed.

While the Rayleigh distribution is useful for quick screening, real-world wind farms typically produce 15-30% differently than simple Rayleigh estimates. Key reasons include:

• Non-Rayleigh distributions: Many sites have Weibull shape factors between 1.5 and 3.0, not exactly 2.0. Coastal sites, mountain passes, and trade-wind regions often deviate significantly from Rayleigh.
• Terrain effects: Hills, ridges, forests, and buildings create turbulence and wind shear that alter both the mean wind speed and its distribution at hub height.
• Wake effects: In multi-turbine wind farms, upstream turbines reduce wind speed for downstream machines by 10-40%, depending on spacing and layout.
• Availability and losses: Real turbines have downtime for maintenance, grid curtailment, icing, and electrical losses that reduce actual production by 5-15%.
• Simplified power curves: Actual turbine power curves are measured in controlled conditions and vary with air density, turbulence intensity, and yaw misalignment.

WindAI's ML model addresses these limitations by training on 10M+ real-world observations from 289 wind farms, capturing site-specific patterns that simple analytical models cannot.

Capacity factor (CF) measures how much energy a wind turbine actually produces compared to its maximum theoretical output. It is calculated as:

A capacity factor of 35% means the turbine produces 35% of the energy it would generate if running at full rated power 24/7 for a year. Typical onshore wind farms achieve 25-45% capacity factor, while offshore farms can reach 40-55%.

Capacity factor is a critical metric for wind farm economics. Higher CF means more energy per dollar of installed capacity, directly improving project returns and levelized cost of energy (LCOE).