Wind rose data¶

Here we demonstrate how mean results over wind rose data are calculated in foxes. We need the following imports:

In [1]:

import matplotlib.pyplot as plt
from plotly.offline import iplot

import foxes
import foxes.variables as FV


First, we setup the model book and the wind farm. We choose 6 x 5 turbines on a regular grid:

In [2]:

mbook = foxes.ModelBook()

farm = foxes.WindFarm()
farm=farm,
xy_base=[0.0, 0.0],
step_vectors=[[900.0, 50.0], [-80., 500.0]],
steps=[6, 5],
turbine_models=["NREL5MW", "kTI_05"],
verbosity=0
)

ax = foxes.output.FarmLayoutOutput(farm).get_figure(figsize=(4, 3))
plt.show()


Next, we create the states based on the static data file wind_rose_bremen.csv.gz. The data represents a (coarse) wind rose with 216 states. Each of them consists of the wind direction and wind speed bin centres, and the respective statistical weight of the bin (normalized such that 1 represents 100%):

state,wd,ws,weight
0,0.0,3.5,0.00158
1,0.0,6.0,0.00244
2,0.0,8.5,0.00319
3,0.0,12.5,0.0036700002
4,0.0,17.5,0.00042
...


Let’s create the states object and have a look at the wind rose:

In [3]:

states = foxes.input.states.StatesTable(
data_source="wind_rose_bremen.csv",
output_vars=[FV.WS, FV.WD, FV.TI, FV.RHO],
var2col={FV.WS: "ws", FV.WD: "wd", FV.WEIGHT: "weight"},
fixed_vars={FV.RHO: 1.225, FV.TI: 0.05},
)

o = foxes.output.StatesRosePlotOutput(states, point=[0., 0., 100.])
fig = o.get_figure(16, FV.AMB_WS, [0, 3.5, 6, 10, 15, 20])
iplot(fig)