def process_reanalysis_data(self):
"""
Process reanalysis data for use in PRUF plant analysis
- calculate density-corrected wind speed
- get monthly average wind speeds
- append monthly averages to monthly energy data frame
Args:
(None)
Returns:
(None)
"""
# Define empty data frame that spans past our period of interest
self._reanalysis_monthly = pd.DataFrame(index=pd.date_range(start='1997-01-01', end='2020-01-01',
freq='MS'))
# Now loop through the different reanalysis products, density-correct wind speeds, and take monthly averages
for key, items in self._plant._reanalysis._product.items():
rean_df = self._plant._reanalysis._product[key].df
rean_df['ws_dens_corr'] = mt.air_density_adjusted_wind_speed(rean_df, 'windspeed_ms',
'rho_kgm-3') # Density correct wind speeds
self._reanalysis_monthly[key] = rean_df.resample('MS')[
'ws_dens_corr'].mean() # .to_frame() # Get average wind speed by year-month
self._monthly.df = self._monthly.df.join(
self._reanalysis_monthly) # Merge monthly reanalysis data to monthly energy data frame
def test_air_density_adjusted_wind_speed(self):
# Test dataframe with wind speed and density data
wind_speed = np.arange(0, 10, 2)
dens = np.arange(1.10, 1.20, 0.02)
adjusted_ws = mt.air_density_adjusted_wind_speed(wind_speed,
dens) # Test answer
adjusted_ws_ans = np.array([0., 1.988235, 4., 6.034885,
8.092494]) # Expected answer
nptest.assert_array_almost_equal(adjusted_ws,
adjusted_ws_ans,
decimal=5)
def test_air_density_adjusted_wind_speed(self):
# Test dataframe with wind speed and density data
df = pd.DataFrame(data={
'wind_speed': np.arange(0, 10, 2),
'dens': np.arange(1.10, 1.20, 0.02)
})
adjusted_ws = mt.air_density_adjusted_wind_speed(
df, 'wind_speed', 'dens') # Test answer
adjusted_ws_ans = np.array([0., 1.988235, 4., 6.034885,
8.092494]) # Expected answer
nptest.assert_array_almost_equal(adjusted_ws,
adjusted_ws_ans,
decimal=5)
def process_reanalysis_data(self):
"""
Process reanalysis data for use in PRUF plant analysis:
- calculate density-corrected wind speed and wind components
- get monthly/daily average wind speeds and components
- calculate monthly/daily average wind direction
- calculate monthly/daily average temperature
- append monthly/daily averages to monthly/daily energy data frame
Args:
(None)
Returns:
(None)
"""
# Define empty data frame that spans past our period of interest
self._reanalysis_aggregate = pd.DataFrame(index=pd.date_range(
start='1997-01-01', end='2019-12-31', freq=self._resample_freq),
dtype=float)
# Now loop through the different reanalysis products, density-correct wind speeds, and take monthly averages
for key in self._reanal_products:
rean_df = self._plant._reanalysis._product[key].df
rean_df['ws_dens_corr'] = mt.air_density_adjusted_wind_speed(
rean_df['windspeed_ms'],
rean_df['rho_kgm-3']) # Density correct wind speeds
self._reanalysis_aggregate[key] = rean_df.resample(
self._resample_freq)['ws_dens_corr'].mean(
) # .to_frame() # Get average wind speed by year-month
if self.reg_winddirection | self.reg_temperature:
namescol = [key + '_' + var for var in self._rean_vars]
self._reanalysis_aggregate[namescol] = rean_df[
self._rean_vars].resample(self._resample_freq).mean()
if self.reg_winddirection: # if wind direction is considered as regression variable
self._reanalysis_aggregate[key + '_wd'] = np.rad2deg(
np.pi -
(np.arctan2(-self._reanalysis_aggregate[key + '_u_ms'],
self._reanalysis_aggregate[key + '_v_ms']))
) # Calculate wind direction
self._aggregate.df = self._aggregate.df.join(
self._reanalysis_aggregate
) # Merge monthly reanalysis data to monthly energy data frame
