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There may also be maintenance and other availability constraints.
This is why capacity factor becomes useful.
In simple terms:
Capacity Factor = Actual Energy Generated / Energy That Would Be Generated at Rated Power Continuously
A turbine’s annual energy production therefore tells us something its nameplate rating cannot.
Power rating tells us how much the machine can produce under specified conditions.
Annual energy tells us what happened over time.
Again, the distinction is between a single number and a distribution through time.
The Same Principle Appears in Solar Energy
The underlying lesson isn’t unique to wind.
A photovoltaic module might be rated at 600 W.
That doesn’t mean it produces 600 W continuously throughout the day.
Solar irradiance changes.
Temperature changes.
Cloud cover changes.
Angle of incidence changes.
Shading can change.
The output is therefore a function of conditions over time.
Similarly, knowing average daily irradiance can be useful, but detailed generation modelling may require understanding when that solar energy arrives and how the system responds to it.
Renewable-energy engineering is full of quantities that vary through time.
Reducing those quantities to averages can be useful.
Treating the averages as the complete reality is not.
The Broader Problem With Averages
This is where wind engineering becomes a lesson in data analysis.
An average is an extraordinarily efficient compression algorithm.
Give me 8,760 hourly wind-speed observations and I can reduce them to one number.
But compression discards information.
The question is whether the discarded information matters to the decision being made.
If the relationship between input and output is linear, an average may preserve much of what we need.
If the relationship is nonlinear, the distribution can become critical.
Wind provides an almost perfect demonstration because:
Power ∝ v³
The outcome is highly sensitive to values away from the mean.
This same analytical mistake appears in many fields.
Average income can hide inequality.
Average temperature can hide extremes.
Average electricity demand can hide the peak that determines required grid capacity.
Average traffic can hide the hour in which a road becomes unusable.
Average rainfall can hide droughts and floods.
And average wind speed can hide the distribution that determines wind-energy potential.
