plot_population

Three pages: (1) individuals / parents / elites,
(2) unique grid cells this generation, in the elites, and ever probed,
(3) park efficiency. Each page waits for Enter in an interactive session.
If the grid has e.g. 70 cells and the whole population still touches
every cell, "this generation" and "ever probed" both sit at 70; the
elite line is then the one that shrinks toward the best sites.

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_population function

<dl><dt>result</dt>
<dd>The output of <code>genetic_algorithm</code></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 pages (Plots pane). Default
is <code>TRUE</code> in an interactive session.</dd></dl>

Arguments

Plot population size, cells and efficiency — plot_population

<dl>

<dt>result</dt>
<dd>The output of <code>genetic_algorithm</code></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 pages (Plots pane). Default
is <code>TRUE</code> in an interactive session.</dd>

</dl>

plot_population: Plot population size, cells and efficiency

Description

Usage

Value

Arguments

See Also

Examples