Plot the best results

Plot the best solutions of the genetic algorithm. Depending on plotEn, either the best energy or efficiency solutions can be plotted. best indicates the amount of best solutions to plot.

Usage

plot_result(
  result,
  Polygon1,
  best = 3,
  plotEn = 1,
  topographie = FALSE,
  Grid = TRUE,
  sourceCCLRoughness = NULL,
  sourceCCL = NULL,
  weibullsrc
)

Arguments

result

The output of windfarmGA or genetic_algorithm

Polygon1

The considered area as SpatialPolygon, SimpleFeature Polygon or coordinates as matrix/data.frame

best

A numeric value indicating how many of the best individuals should be plotted

plotEn

A numeric value that indicates if the best energy or efficiency output should be plotted. 1 plots the best energy solutions and 2 plots the best efficiency solutions

topographie

A logical value, indicating whether terrain effects should be considered and plotted or not

Grid

If TRUE (default) the used grid will be added to the plot. You can also pass another Simple Feature object

sourceCCLRoughness

The source to the adapted Corine Land Cover legend as .csv file. Only required when terrain effect model is activated. As default a .csv file within this package (~/extdata) is taken that was already adapted manually.

sourceCCL

The path to the Corine Land Cover raster (.tif). Only required when the terrain effect model is activated.

weibullsrc

A list of Weibull parameter rasters, where the first list item must be the shape parameter raster `k` and the second item must be the scale parameter raster `a` of the Weibull distribution. If no list is given, then rasters included in the package are used instead, which currently only cover Austria. This variable is only used if weibull = TRUE.

Value

Returns a data.frame of the best (energy/efficiency) individual during all iterations

See also

Other Plotting Functions: interpol_view(), plot_cloud(), plot_development(), plot_evolution(), plot_fitness_evolution(), plot_heatmap(), plot_parkfitness(), plot_viewshed(), plot_windfarmGA(), plot_windrose(), random_search_single()

Examples

# \donttest{
## Add some data examples from the package
library(sf)
Polygon1 <- sf::st_as_sf(sf::st_sfc(
  sf::st_polygon(list(cbind(
    c(4498482, 4498482, 4499991, 4499991, 4498482),
    c(2668272, 2669343, 2669343, 2668272, 2668272)))), 
  crs = 3035
))

## Plot the results of a hexagonal grid optimization
plot_result(resulthex, Polygon1, best = 1, plotEn = 1, topographie = FALSE)
#> N different optimal configurations: 7 
#> Amount duplicates: 3
#> Plot  1  Best  Energy  Solution:


## Plot the results of a rectangular grid optimization
plot_result(resultrect, Polygon1, best = 1, plotEn = 1, topographie = FALSE)
#> N different optimal configurations: 108 
#> Amount duplicates: 92
#> Plot  1  Best  Energy  Solution:

# }