plot_parkfitness

Fitness (max / mean / min) and the three operator rates.
Rates are percentages: Selection = share of the population used as
parents, Crossover inject = unused cells mixed into children,
Mutation = chance that a turbine is swapped to a free cell.
The legend sits outside and follows the typical vertical order of the
lines (selection highest, then inject, mutation lowest). Plotly hover
is used only when <code>ask</code> is <code>FALSE</code> and plotly is installed.

The genetic algorithm is designed to optimize wind farms of any shape. Each layout is encoded as n unique grid-cell identifiers. It requires a predefined amount of turbines, a unified rotor radius and an average wind speed value for each incoming wind direction. A terrain effect model can be included that downloads an 'SRTM' elevation model and loads a Corine Land Cover raster to approximate surface roughness.

Sebastian Gatscha

windfarmGA

Genetic Algorithm for Wind Farm Layout Optimization

plot_parkfitness function

<dl><dt>result</dt>
<dd>The output of <code>genetic_algorithm</code></dd>
<dt>spar</dt>
<dd>Unused, kept so existing calls do not break.</dd>
<dt>interactive</dt>
<dd>Use plotly when <code>ask</code> is <code>FALSE</code> and plotly is installed.</dd>
<dt>ask</dt>
<dd>If <code>TRUE</code>, wait for Enter between the fitness page and the
rates page. Default is <code>TRUE</code> in an interactive session.</dd></dl>

Arguments

Fitness and operator rates — plot_parkfitness

<dl>

<dt>result</dt>
<dd>The output of <code>genetic_algorithm</code></dd>


<dt>spar</dt>
<dd>Unused, kept so existing calls do not break.</dd>


<dt>interactive</dt>
<dd>Use plotly when <code>ask</code> is <code>FALSE</code> and plotly is installed.</dd>


<dt>ask</dt>
<dd>If <code>TRUE</code>, wait for Enter between the fitness page and the
rates page. Default is <code>TRUE</code> in an interactive session.</dd>

</dl>

plot_parkfitness: Fitness and operator rates

Description

Usage

Value

Arguments

See Also

Examples