Genetic Algorithm for Wind Farm Layout Optimization
Calculates Air Density, Air Pressure and Temperature according to the ...
Calculate Energy Outputs of Individuals
Get area of intersecting circles
Crossover Method
Evaluate the Individual Fitness values
Run a Genetic Algorithm to optimize a wind farm layout
Calculate distances and angles of possibly influencing turbines
Get the Grid-IDs from binary matrix
Make a grid from a Simple Feature Polygon
Polygon to Hexagonal Grids
Create a random initial Population
Transform to Simple Feature Polygons
Mutation Method
Is the package installed or not
Enumerate the Combinations or Permutations of the Elements of a Vector
Plot outputs of all generations with standard deviations
Plot the progress of populations
Plot the evolution of fitness values
Plot the changes of min/mean/max fitness values
Plot a wind warm with leaflet
Plot the genetic algorithm results
Plot the result of a randomized output.
Plot the best results
Plot visibility
Plot the results of an optimization run
Plot a Windrose
Randomize the location of a single turbine
Randomize the output of the Genetic Algorithm
Check Input Crossover Method
Check Input Selection Method
Selection Method
Split matrices or numeric vectors at specific indices
Get topographic rasters
Adjust the amount of turbines per windfarm
Find potentially influencing turbines
Transform Winddata
windfarmGA: Genetic Algorithm for Wind Farm Layout Optimization
The genetic algorithm is designed to optimize wind farms of any shape. 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.
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