Adds classes to structure wind energy data for FLORIS

examples/34_wind_data.py

@@ -0,0 +1,84 @@
+# Copyright 2024 NREL
+
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+# See https://floris.readthedocs.io for documentation
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from floris.tools import (
+    FlorisInterface,
+    TimeSeries,
+    WindRose,
+)
+from floris.utilities import wrap_360
+
+
+"""
+This example is meant to be temporary and may be updated by a later pull request. Before we
+release v4, we intend to propagate the TimeSeries and WindRose objects through the other relevant
+examples, and change this example to demonstrate more advanced (as yet, not implemented)
+functionality of the WindData objects (such as electricity pricing etc).
+"""
+
+
+# Generate a random time series of wind speeds, wind directions and turbulence intensities
+N = 500
+wd_array = wrap_360(270 * np.ones(N) + np.random.randn(N) * 20)
+ws_array = np.clip(8 * np.ones(N) + np.random.randn(N) * 8, 3, 50)
+ti_array = np.clip(0.1 * np.ones(N) + np.random.randn(N) * 0.05, 0, 0.25)
+
+fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(7, 4))
+ax = axarr[0]
+ax.plot(wd_array, marker=".", ls="None")
+ax.set_ylabel("Wind Direction")
+ax = axarr[1]
+ax.plot(ws_array, marker=".", ls="None")
+ax.set_ylabel("Wind Speed")
+ax = axarr[2]
+ax.plot(ti_array, marker=".", ls="None")
+ax.set_ylabel("Turbulence Intensity")
+
+
+# Build the time series
+time_series = TimeSeries(wd_array, ws_array)  # , turbulence_intensity=ti_array)
+
+# Now build the wind rose
+wind_rose = time_series.to_wind_rose()
+
+# Plot the wind rose
+fig, ax = plt.subplots(subplot_kw={"polar": True})
+wind_rose.plot_wind_rose(ax=ax)
+
+# Now set up a FLORIS model and initialize it using the time series and wind rose
+fi = FlorisInterface("inputs/gch.yaml")
+fi.reinitialize(layout_x=[0, 500.0], layout_y=[0.0, 0.0])
+
+fi_time_series = fi.copy()
+fi_wind_rose = fi.copy()
+
+fi_time_series.reinitialize(wind_data=time_series)
+fi_wind_rose.reinitialize(wind_data=wind_rose)
+
+fi_time_series.calculate_wake()
+fi_wind_rose.calculate_wake()
+
+time_series_power = fi_time_series.get_farm_power()
+wind_rose_power = fi_wind_rose.get_farm_power()
+
+time_series_aep = fi_time_series.get_farm_AEP_with_wind_data(time_series)
+wind_rose_aep = fi_wind_rose.get_farm_AEP_with_wind_data(wind_rose)
+
+print(f"AEP from TimeSeries {time_series_aep / 1e9:.2f} GWh")
+print(f"AEP from WindRose {wind_rose_aep / 1e9:.2f} GWh")
+
+plt.show()

floris/tools/__init__.py

@@ -46,18 +46,22 @@
     visualize_cut_plane,
     visualize_quiver,
 )
-from .wind_rose import WindRose
+from .wind_data import (
+    TimeSeries,
+    WindDataBase,
+    WindRose,
+)
 
 
 # from floris.tools import (
-    # cut_plane,
-    # floris_interface,
-    # interface_utilities,
-    # layout_functions,
-    # optimization,
-    # plotting,
-    # power_rose,
-    # rews,
-    # visualization,
-    # wind_rose,
+# cut_plane,
+# floris_interface,
+# interface_utilities,
+# layout_functions,
+# optimization,
+# plotting,
+# power_rose,
+# rews,
+# visualization,
+# wind_rose,
 # )