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System module sphinx documentation (#146)
* Added dummy files for the system module documentation. * Added new examples for system tracking. * updated the system tracking docs. * adding docs * updates to the routines for system module * updated the routines for tracking module * cleaned up doc strings + added example SERF East data. * updated the whatsnew docs * Updated the pvwatts routine to use PSM3 data * added psm3 csv * reduced size of the serf east fiel to 10k rows * removed duplicate csv file * added the system ID to OEDI documentation * update the system ID for OEDI * Update docs/examples/infer-orientation-daily-peak.py Co-authored-by: Kevin Anderson <[email protected]> * Update docs/examples/infer-orientation-fit-pvwatts.py Co-authored-by: Kevin Anderson <[email protected]> * changes to documentation based on @kandersonrel's review * Update docs/examples/infer-orientation-fit-pvwatts.py Co-authored-by: Kevin Anderson <[email protected]> * updated the docstring to avoid pep8 errors> * update the csv name * removed the daily peak documentation. * reformatting for release 0.1.3 * updated pvwatts model docstring * added oedi reference to tracking system documentation * removed redundant file directory definition * Added readme for the system examples * update the naming for pvwatts model * Add visualizations for the documentation (plot first few days of time series) * updated docstrings in final review * fix flake8 issues * added back missing text in whats new 0.1.2 * Update docs/examples/system/infer-orientation-fit-pvwatts.py Co-authored-by: Cliff Hansen <[email protected]> Co-authored-by: Perry <[email protected]> Co-authored-by: Kevin Anderson <[email protected]> Co-authored-by: Cliff Hansen <[email protected]>
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‎docs/examples/system/README.rst‎

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System
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------
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This includes examples for system parameter estimation, including azimuth and tilt estimation, and determination if the system is fixed tilt or tracking.

‎docs/examples/system/infer-orientation-fit-pvwatts.py‎

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"""
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Infer Array Tilt/Azimuth - PVWatts Method
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=========================================
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Infer the azimuth and tilt of a system using PVWatts-based methods
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"""
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# %%
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# Identifing and/or validating the azimuth and tilt information for a
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# system is important, as these values must be correct for degradation
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# and system yield analysis. This example shows how to use
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# :py:func:`pvanalytics.system.infer_orientation_fit_pvwatts` to estimate
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# a fixed-tilt system's azimuth and tilt, using the system's known
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# latitude-longitude coordinates and an associated AC power time series.
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import pvanalytics
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import matplotlib.pyplot as plt
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from pvanalytics import system
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import pandas as pd
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import pathlib
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import pvlib
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# %%
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# First, we import an AC power data stream from the SERF East site located at
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# NREL. This data set is publicly available via the PVDAQ database in the
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# DOE Open Energy Data Initiative (OEDI)
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# (https://data.openei.org/submissions/4568), under system ID 50.
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# This data is timezone-localized.
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pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
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ac_power_file = pvanalytics_dir / 'data' / 'serf_east_15min_ac_power.csv'
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data = pd.read_csv(ac_power_file, index_col=0, parse_dates=True)
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data = data.sort_index()
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time_series = data['ac_power']
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time_series = time_series.asfreq('15T')
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# Plot the first few days of the time series to visualize it
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time_series[:pd.to_datetime("2016-07-06 00:00:00-07:00")].plot()
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plt.show()
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# Outline the ground truth metadata associated with the system
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latitude = 39.742
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longitude = -105.1727
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actual_azimuth = 158
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actual_tilt = 45
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# %%
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# Next, we import the PSM3 data generated via the
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# :py:func:`pvlib.iotools.get_psm3` function, using
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# site latitude-longitude coordinates. To generate the
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# PSM3 data, you must first register for NREL's NSDRB API at the
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# following link: https://developer.nrel.gov/signup/.
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# PSM3 data can then be retrieved using :py:func:`pvlib.iotools.get_psm3`.
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# The PSM3 data has been resampled to 15 minute intervals, to match the AC
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# power data.
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psm3_file = pvanalytics_dir / 'data' / 'serf_east_psm3_data.csv'
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psm3 = pd.read_csv(psm3_file, index_col=0, parse_dates=True)
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# %%
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# Filter the PSM3 data to only include clearsky periods
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is_clear = (psm3.ghi_clear == psm3.ghi)
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is_daytime = (psm3.ghi > 0)
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time_series_clearsky = time_series[is_clear & is_daytime]
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time_series_clearsky = time_series_clearsky.dropna()
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psm3_clearsky = psm3.loc[time_series_clearsky.index]
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# Get solar azimuth and zenith from pvlib, based on
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# lat-long coords
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solpos_clearsky = pvlib.solarposition.get_solarposition(
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time_series_clearsky.index, latitude, longitude)
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# %%
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# Run the pvlib data and the sensor-based time series data through
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# the :py:func:`pvanalytics.system.infer_orientation_fit_pvwatts` function.
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best_tilt, best_azimuth, r2 = system.infer_orientation_fit_pvwatts(
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time_series_clearsky,
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psm3_clearsky.ghi_clear,
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psm3_clearsky.dhi_clear,
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psm3_clearsky.dni_clear,
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solpos_clearsky.zenith,
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solpos_clearsky.azimuth,
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temperature=psm3_clearsky.temp_air,
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)
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# Compare actual system azimuth and tilt to predicted azimuth and tilt
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print("Actual Azimuth: " + str(actual_azimuth))
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print("Predicted Azimuth: " + str(best_azimuth))
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print("Actual Tilt: " + str(actual_tilt))
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print("Predicted Tilt: " + str(best_tilt))

‎docs/examples/system/system-tracking.py‎

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"""
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Detect if a System is Tracking
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==============================
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Identifying if a system is tracking or fixed tilt
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"""
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# %%
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# It is valuable to identify if a system is fixed tilt or tracking for
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# future analysis. This example shows how to use
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# :py:func:`pvanalytics.system.is_tracking_envelope` to determine if a
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# system is tracking or not by fitting data to a maximum power or
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# irradiance envelope, and fitting this envelope to quadratic and
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# quartic curves. The r^2 output from these fits is used to determine
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# if the system fits a tracking or fixed-tilt profile.
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import pvanalytics
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from pvanalytics.system import is_tracking_envelope
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from pvanalytics.features.clipping import geometric
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from pvanalytics.features.daytime import power_or_irradiance
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import pandas as pd
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import pathlib
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import matplotlib.pyplot as plt
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# %%
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# First, we import an AC power data stream from the SERF East site located at
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# NREL. This data set is publicly available via the PVDAQ database in the
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# DOE Open Energy Data Initiative (OEDI)
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# (https://data.openei.org/submissions/4568), under system ID 50.
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# This data is timezone-localized. This particular data stream is associated
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# with a fixed-tilt system.
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pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
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ac_power_file = pvanalytics_dir / 'data' / \
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'serf_east_15min_ac_power.csv'
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data = pd.read_csv(ac_power_file, index_col=0, parse_dates=True)
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data = data.sort_index()
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time_series = data['ac_power']
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time_series = time_series.asfreq('15T')
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# Plot the first few days of the time series to visualize it
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time_series[:pd.to_datetime("2016-07-06 00:00:00-07:00")].plot()
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plt.show()
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# %%
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# Run the clipping and the daytime filters on the time series.
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# Both of these masks will be used as inputs to the
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# :py:func:`pvanalytics.system.is_tracking_envelope` function.
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# Generate the daylight mask for the AC power time series
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daytime_mask = power_or_irradiance(time_series)
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# Generate the clipping mask for the time series
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clipping_mask = geometric(time_series)
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# %%
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# Now, we use :py:func:`pvanalytics.system.is_tracking_envelope` to
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# identify if the data stream is associated with a tracking or fixed-tilt
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# system.
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predicted_mounting_config = is_tracking_envelope(time_series,
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daytime_mask,
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clipping_mask)
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print("Estimated mounting configuration: " + predicted_mounting_config.name)

‎docs/whatsnew/0.1.2.rst‎

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* Removed empty modules ``pvanalytics.filtering`` and ``pvanalytics.fitting``
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until the relevant functionality is added to the package. (:pull:`145`)
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Contributors
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~~~~~~~~~~~~
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‎docs/whatsnew/0.1.3.rst‎

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* :py:func:`~pvanalytics.quality.weather.relative_humidity_limits`
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* :py:func:`~pvanalytics.quality.weather.wind_limits`
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* :py:func:`~pvanalytics.quality.weather.module_temperature_check`
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* ``pvanalytics.system`` (:issue:`133`, :pull:`146`):
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* :py:func:`~pvanalytics.system.infer_orientation_fit_pvwatts`
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* :py:func:`~pvanalytics.system.is_tracking_envelope`
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* ``pvanalytics.quality.irradiance.calculate_component_sum_series``(:issue:`157`, :pull:`163`)
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