diff --git a/docs/examples/fixed-nrel.py b/docs/examples/fixed-nrel.py new file mode 100644 index 00000000..80bcf4e8 --- /dev/null +++ b/docs/examples/fixed-nrel.py @@ -0,0 +1,55 @@ +""" +Flag Sunny Days for a Fixed-Tilt System +======================================= + +Flag sunny days for a fixed-tilt PV system. +""" + +# %% +# Identifying and masking sunny days for a fixed-tilt system is important +# when performing future analyses that require filtered sunny day data. +# For this example we will use data from the fixed-tilt NREL SERF East system +# located on the NREL campus in Colorado, USA, and generate a sunny day mask. +# This data set is publicly available via the PVDAQ database in the +# DOE Open Energy Data Initiative (OEDI) +# (https://data.openei.org/submissions/4568), as system ID 50. +# This data is timezone-localized. + +import pvanalytics +from pvanalytics.features import daytime as day +from pvanalytics.features.orientation import fixed_nrel +import matplotlib.pyplot as plt +import pandas as pd +import pathlib + +# %% +# First, read in data from the NREL SERF East fixed-tilt system. This data +# set contains 15-minute interval AC power data. + +pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent +file = pvanalytics_dir / 'data' / 'serf_east_15min_ac_power.csv' +data = pd.read_csv(file, index_col=0, parse_dates=True) + + +# %% +# Mask day-night periods using the +# :py:func:`pvanalytics.features.daytime.power_or_irradiance` function. +# Then apply :py:func:`pvanalytics.features.orientation.fixed_nrel` +# to the AC power stream and mask the sunny days in the time series. + +daytime_mask = day.power_or_irradiance(data['ac_power']) + +fixed_sunny_days = fixed_nrel(data['ac_power'], + daytime_mask) + +# %% +# Plot the AC power stream with the sunny day mask applied to it. + +data['ac_power'].plot() +data.loc[fixed_sunny_days, 'ac_power'].plot(ls='', marker='.') +plt.legend(labels=["AC Power", "Sunny Day"], + loc="upper left") +plt.xlabel("Date") +plt.ylabel("AC Power (kW)") +plt.tight_layout() +plt.show() diff --git a/docs/examples/tracking-nrel.py b/docs/examples/tracking-nrel.py new file mode 100644 index 00000000..633ec0b4 --- /dev/null +++ b/docs/examples/tracking-nrel.py @@ -0,0 +1,55 @@ +""" +Flag Sunny Days for a Tracking System +===================================== + +Flag sunny days for a single-axis tracking PV system. +""" + +# %% +# Identifying and masking sunny days for a single-axis tracking system is +# important when performing future analyses that require filtered sunny day +# data. For this example we will use data from the single-axis tracking +# NREL Mesa system located on the NREL campus in Colorado, USA, and generate +# a sunny day mask. +# This data set is publicly available via the PVDAQ database in the +# DOE Open Energy Data Initiative (OEDI) +# (https://data.openei.org/submissions/4568), as system ID 50. +# This data is timezone-localized. + +import pvanalytics +from pvanalytics.features import daytime as day +from pvanalytics.features.orientation import tracking_nrel +import matplotlib.pyplot as plt +import pandas as pd +import pathlib + +# %% +# First, read in data from the NREL Mesa 1-axis tracking system. This data +# set contains 15-minute interval AC power data. + +pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent +file = pvanalytics_dir / 'data' / 'nrel_1axis_tracker_mesa_ac_power.csv' +data = pd.read_csv(file, index_col=0, parse_dates=True) + +# %% +# Mask day-night periods using the +# :py:func:`pvanalytics.features.daytime.power_or_irradiance` function. +# Then apply :py:func:`pvanalytics.features.orientation.tracking_nrel` +# to the AC power stream and mask the sunny days in the time series. + +daytime_mask = day.power_or_irradiance(data['ac_power']) + +tracking_sunny_days = tracking_nrel(data['ac_power'], + daytime_mask) + +# %% +# Plot the AC power stream with the sunny day mask applied to it. + +data['ac_power'].plot() +data.loc[tracking_sunny_days, 'ac_power'].plot(ls='', marker='.') +plt.legend(labels=["AC Power", "Sunny Day"], + loc="upper left") +plt.xlabel("Date") +plt.ylabel("AC Power (kW)") +plt.tight_layout() +plt.show() diff --git a/docs/whatsnew/0.1.2.rst b/docs/whatsnew/0.1.2.rst index e3da05ad..ddfb3bfe 100644 --- a/docs/whatsnew/0.1.2.rst +++ b/docs/whatsnew/0.1.2.rst @@ -46,10 +46,13 @@ Documentation :py:func:`pvanalytics.quality.irradiance.check_irradiance_consistency_qcrad`, and :py:func:`pvanalytics.quality.irradiance.check_irradiance_limits_qcrad` (:issue:`133`, :pull:`140`) +* Added examples for the pvanalytics.features.orientation module, + including :py:func:`pvanalytics.features.orientation.fixed_nrel`, + and :py:func:`pvanalytics.quality.features.orientation.tracking_nrel` + (:issue:`133`, :pull:`148`) * Added an example for :py:func:`pvanalytics.quality.data_shifts` routine (:pull:`131`) - Contributors ~~~~~~~~~~~~ diff --git a/pvanalytics/data/nrel_1axis_tracker_mesa_ac_power.csv b/pvanalytics/data/nrel_1axis_tracker_mesa_ac_power.csv new file mode 100644 index 00000000..979035c2 --- /dev/null +++ b/pvanalytics/data/nrel_1axis_tracker_mesa_ac_power.csv @@ -0,0 +1,577 @@ +measured_on,ac_power +2010-06-25 00:00:00-06:00,0.0 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