windfarmGA (version 2.3.0)

fitness: Evaluate the Individual Fitness values

Description

The fitness values of the individuals in the current population are calculated after having evaluated their energy outputs in calculate_energy. This function reduces the resulting energy outputs to a single fitness value for every 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.

RotorHeight

The desired height of the turbine.

SurfaceRoughness

A surface roughness length of the considered area in m.

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

Logical value that indicates whether the terrain effect model is activated (TRUE) or deactivated (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

Boolean value, indicating whether parallel processing should be used. The parallel and doParallel packages are used for parallel processing.

numCluster

If Parallel is TRUE, this variable defines the number of clusters to be used.

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(), windfarmGA()

Examples

# NOT RUN {
## Create a random rectangular shapefile
library(sp)
Polygon1 <- Polygon(rbind(c(4498482, 2668272), c(4498482, 2669343),
                    c(4499991, 2669343), c(4499991, 2668272)))
Polygon1 <- Polygons(list(Polygon1),1);
Polygon1 <- SpatialPolygons(list(Polygon1))
Projection <- "+init=epsg:3035"
proj4string(Polygon1) <- CRS(Projection)

## 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,resol = 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)
# }