"""Provides the FLORIS-based methods for pre-processing data and post-processing results."""
from __future__ import annotations
import numpy as np
import pandas as pd
from floris import WindRose, TimeSeries
[docs]
def create_single_month_wind_rose(weather_df: pd.DataFrame, month: int) -> tuple[int, WindRose]:
"""Creates the FLORIS ``WindRose`` object for a given :py:attr:`month` based on the
:py:attr:`weather_df`'s ``DatetimeIndex``.
Parameters
----------
weather_df : pd.DataFrame
The weather profile used to create long-term, month-based ``WindRose`` objects
month : int
The month of the year to create a ``WindRose`` object.
Returns
-------
WindRose
Month-specific ``WindRose`` object.
"""
wd, ws, ti = weather_df.loc[weather_df.index.month == month].values.T
wind_rose = TimeSeries(
wind_directions=wd, wind_speeds=ws, turbulence_intensities=ti
).to_WindRose()
return wind_rose
[docs]
def create_monthly_wind_rose(weather_df: pd.DataFrame) -> dict[int, WindRose]:
"""Create a dictionary of month and a long-term ``WindRose`` object based on all the
wind condition data for that month.
Parameters
----------
weather_df : pd.DataFrame
The weather profile used to create long-term, month-based ``WindRose`` objects
Returns
-------
dict[int, WindRose]
A dictionary of the integer month and the long-term ``WindRose`` object associated
with all the wind conditions during that month.
"""
monthly_wr = {
month: create_single_month_wind_rose(weather_df=weather_df, month=month)
for month in range(1, 13)
}
return monthly_wr
[docs]
def check_monthly_wind_rose(
project_wind_rose: WindRose, monthly_wind_rose: dict[int, WindRose]
) -> dict[int, WindRose]:
"""Checks the monthly wind rose parameterizations to ensure the DataFrames are the
correct shape, so that when the frequency column is extracted, the compared data
is the same.
Parameters
----------
project_wind_rose : WindRose
The ``WindRose`` created using the long term reanalysis weather profile.
monthly_wind_rose : dict[int, WindRose]
A dictionary of the month as an ``int`` and ``WindRose`` created from the long
term project reanalysis weather profile that was filtered on weather data for
the focal month.
Returns
-------
dict[int, WindRose]
The :py:attr:`monthly_wind_rose` but with an missing wind conditions added into
the ``WindRose`` with 0 frequency.
"""
project_df = pd.DataFrame(
project_wind_rose.freq_table,
columns=project_wind_rose.wind_speeds,
index=project_wind_rose.wind_directions,
)
project_shape = project_df.shape
project_wd = project_wind_rose.wind_directions
project_ws = project_wind_rose.wind_speeds
for month, wind_rose in monthly_wind_rose.items():
if wind_rose.freq_table.shape == project_shape:
continue
wr_df = pd.DataFrame(
wind_rose.freq_table, columns=wind_rose.wind_speeds, index=wind_rose.wind_directions
)
ti_df = pd.DataFrame(
wind_rose.ti_table, columns=wind_rose.wind_speeds, index=wind_rose.wind_directions
)
# Find the missing combinations, add them to the wind rose DataFrame, and resort
missing_wd = list(set(project_wd).difference(wind_rose.wind_directions))
missing_ws = list(set(project_ws).difference(wind_rose.wind_speeds))
wr_df.loc[missing_wd] = 0
wr_df.loc[:, missing_ws] = 0
wr_df = wr_df.sort_index().T.sort_index().T
if wr_df.shape != project_shape:
raise ValueError("The monthly wind rose was unable to be resampled.")
ti_df.loc[missing_wd] = 0
ti_df.loc[:, missing_ws] = 0
ti_df = ti_df.sort_index().T.sort_index().T
if ti_df.shape != project_shape:
raise ValueError("The monthly wind rose was unable to be resampled.")
# Recreate the WindRose object with the missing wd/ws values added back in
monthly_wind_rose[month] = WindRose(
wind_directions=wr_df.index.values,
wind_speeds=wr_df.columns.values,
freq_table=wr_df.values,
ti_table=ti_df.values,
)
return monthly_wind_rose
[docs]
def calculate_monthly_wind_rose_results(
turbine_power: np.ndarray,
wind_rose_monthly: dict[int, WindRose],
) -> pd.DataFrame:
"""Calculate the turbine AEP contribution for each month of a year, in MWh.
Parameters
----------
turbine_power : np.ndarray
The array of turbine powers, with shape (num wd x num ws x num turbines), calculated from
the possible wind conditions at the site given the turbine layout.
wind_rose_monthly : dict[int, WindRose]
The dictionary of integer months (i.e., 1 for January) and array of frequences, with
``WindRose`` objects created by the long term wind conditions filtered on the month.
Returns
-------
pd.DataFrame, pd.DataFrame
A DataFrame of each month's contribution to the AEP for each turbine in the wind farm, with
shape (12 x num turbines).
"""
month_day_map = pd.DataFrame(
[
[1, 31],
[2, 28],
[3, 31],
[4, 30],
[5, 31],
[6, 30],
[7, 31],
[8, 31],
[9, 30],
[10, 31],
[11, 30],
[12, 31],
],
columns=["month", "n_days"],
).set_index("month")
# Calculate the monthly turbine energy and sum over the turbines to get the farm energy
turbine_energy = pd.DataFrame.from_dict(
{
m: np.nansum(mwr[:, :, np.newaxis] * turbine_power, axis=(0, 1))
for m, mwr in wind_rose_monthly.items()
},
orient="index",
).sort_index()
turbine_energy *= month_day_map.n_days.values.reshape(12, 1) * 24 / 1e6
return turbine_energy
