fitness: Evaluate the Individual Fitness values

In windfarmGA: Genetic Algorithm for Wind Farm Layout Optimization

Evaluate the Individual Fitness values

Description

The fitness of all individuals in the current population is calculated after their energy output has been evaluated in calculate_energy. This function reduces the resulting energy outputs to a single fitness value for each individual.

Usage

fitness(
  selection,
  referenceHeight,
  RotorHeight,
  SurfaceRoughness,
  Polygon,
  resol1,
  rot,
  dirspeed,
  srtm_crop,
  topograp,
  cclRaster,
  weibull,
  Parallel,
  numCluster
)

Arguments

selection	A list containing all individuals of the current population.
referenceHeight	The height at which the incoming wind speeds were measured. Default is RotorHeight
RotorHeight	The height of the turbine hub
SurfaceRoughness	A surface roughness length in meters. With the terrain effect model, a surface roughness is calculated for every grid cell using the elevation and land cover data. Default is 0.3
Polygon	The considered area as shapefile.
resol1	The resolution of the grid in meter.
rot	The desired rotor radius in meter.
dirspeed	The wind data as list.
srtm_crop	A list of 3 raster, with 1) the elevation, 2) an orographic and 3) a terrain raster. Calculated in genetic_algorithm
topograp	Boolean value, which indicates if the terrain effect model should be enabled or not. Default is FALSE
cclRaster	A Corine Land Cover raster, that has to be adapted previously by hand with the surface roughness length for every land cover type. Is only used, when the terrain effect model is activated.
weibull	A raster representing the estimated wind speeds
Parallel	A boolean value, indicating whether parallel processing should be used. The parallel and doParallel packages are used for parallel processing. Default is FALSE
numCluster	If Parallel is TRUE, this variable defines the number of clusters to be used. Default is 2

Value

Returns a list with every individual, consisting of X & Y coordinates, rotor radii, the runs and the selected grid cell IDs, and the resulting energy outputs, efficiency rates and fitness values.

See Also

Other Genetic Algorithm Functions: crossover(), genetic_algorithm(), init_population(), mutation(), selection(), trimton()

Examples

## Create a random rectangular shapefile
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
))

## Create a uniform and unidirectional wind data.frame and plots the
## resulting wind rose
## Uniform wind speed and single wind direction
wind <- data.frame(ws = 12, wd = 0)
# windrosePlot <- plot_windrose(data = wind, spd = wind$ws,
#                dir = wind$wd, dirres=10, spdmax=20)

## Calculate a Grid and an indexed data.frame with coordinates and
## grid cell IDs.
Grid1 <- grid_area(shape = Polygon1, size = 200, prop = 1)
Grid <- Grid1[[1]]
AmountGrids <- nrow(Grid)

wind <- list(wind, probab = 100)
startsel <- init_population(Grid, 10, 20)
fit <- fitness(
  selection = startsel, referenceHeight = 100, RotorHeight = 100,
  SurfaceRoughness = 0.3, Polygon = Polygon1, resol1 = 200, rot = 20,
  dirspeed = wind, srtm_crop = "", topograp = FALSE, cclRaster = "",
  Parallel = FALSE
)

windfarmGA documentation built on April 4, 2025, 3:39 a.m.