.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/gallery/system/infer-orientation-fit-pvwatts.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_generated_gallery_system_infer-orientation-fit-pvwatts.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_generated_gallery_system_infer-orientation-fit-pvwatts.py:


Infer Array Tilt/Azimuth - PVWatts Method
=========================================

Infer the azimuth and tilt of a system using PVWatts-based methods

.. GENERATED FROM PYTHON SOURCE LINES 9-15

Identifing and/or validating  the azimuth and tilt information for a
system is important, as these values must be correct for degradation
and system yield analysis. This example shows how to use
:py:func:`pvanalytics.system.infer_orientation_fit_pvwatts` to estimate
a fixed-tilt system's azimuth and tilt, using the system's known
latitude-longitude coordinates and an associated AC power time series.

.. GENERATED FROM PYTHON SOURCE LINES 15-23

.. code-block:: default


    import pvanalytics
    import matplotlib.pyplot as plt
    from pvanalytics import system
    import pandas as pd
    import pathlib
    import pvlib








.. GENERATED FROM PYTHON SOURCE LINES 24-29

First, we import an AC power data stream from the SERF East site located at
NREL. This data set is publicly available via the PVDAQ database in the
DOE Open Energy Data Initiative (OEDI)
(https://data.openei.org/submissions/4568), under system ID 50.
This data is timezone-localized.

.. GENERATED FROM PYTHON SOURCE LINES 29-47

.. code-block:: default


    pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
    ac_power_file = pvanalytics_dir / 'data' / 'serf_east_15min_ac_power.csv'
    data = pd.read_csv(ac_power_file, index_col=0, parse_dates=True)
    data = data.sort_index()
    time_series = data['ac_power']
    time_series = time_series.asfreq('15T')

    # Plot the first few days of the time series to visualize it
    time_series[:pd.to_datetime("2016-07-06 00:00:00-07:00")].plot()
    plt.show()

    # Outline the ground truth metadata associated with the system
    latitude = 39.742
    longitude = -105.1727
    actual_azimuth = 158
    actual_tilt = 45




.. image-sg:: /generated/gallery/system/images/sphx_glr_infer-orientation-fit-pvwatts_001.png
   :alt: infer orientation fit pvwatts
   :srcset: /generated/gallery/system/images/sphx_glr_infer-orientation-fit-pvwatts_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 48-56

Next, we import the PSM3 data generated via the
:py:func:`pvlib.iotools.get_psm3` function, using
site latitude-longitude coordinates. To generate the
PSM3 data, you must first register for NREL's NSDRB API at the
following link: https://developer.nrel.gov/signup/.
PSM3 data can then be retrieved using :py:func:`pvlib.iotools.get_psm3`.
The PSM3 data has been resampled to 15 minute intervals, to match the AC
power data.

.. GENERATED FROM PYTHON SOURCE LINES 56-60

.. code-block:: default


    psm3_file = pvanalytics_dir / 'data' / 'serf_east_psm3_data.csv'
    psm3 = pd.read_csv(psm3_file, index_col=0, parse_dates=True)








.. GENERATED FROM PYTHON SOURCE LINES 61-62

Filter the PSM3 data to only include clearsky periods

.. GENERATED FROM PYTHON SOURCE LINES 62-73

.. code-block:: default

    is_clear = (psm3.ghi_clear == psm3.ghi)
    is_daytime = (psm3.ghi > 0)
    time_series_clearsky = time_series[is_clear & is_daytime]
    time_series_clearsky = time_series_clearsky.dropna()
    psm3_clearsky = psm3.loc[time_series_clearsky.index]

    # Get solar azimuth and zenith from pvlib, based on
    # lat-long coords
    solpos_clearsky = pvlib.solarposition.get_solarposition(
        time_series_clearsky.index, latitude, longitude)








.. GENERATED FROM PYTHON SOURCE LINES 74-76

Run the pvlib data and the sensor-based time series data through
the :py:func:`pvanalytics.system.infer_orientation_fit_pvwatts` function.

.. GENERATED FROM PYTHON SOURCE LINES 76-91

.. code-block:: default

    best_tilt, best_azimuth, r2 = system.infer_orientation_fit_pvwatts(
        time_series_clearsky,
        psm3_clearsky.ghi_clear,
        psm3_clearsky.dhi_clear,
        psm3_clearsky.dni_clear,
        solpos_clearsky.zenith,
        solpos_clearsky.azimuth,
        temperature=psm3_clearsky.temp_air,
    )

    # Compare actual system azimuth and tilt to predicted azimuth and tilt
    print("Actual Azimuth: " + str(actual_azimuth))
    print("Predicted Azimuth: " + str(best_azimuth))
    print("Actual Tilt: " + str(actual_tilt))
    print("Predicted Tilt: " + str(best_tilt))




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Actual Azimuth: 158
    Predicted Azimuth: 161.07038200206492
    Actual Tilt: 45
    Predicted Tilt: 45.37814701121294





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.878 seconds)


.. _sphx_glr_download_generated_gallery_system_infer-orientation-fit-pvwatts.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: infer-orientation-fit-pvwatts.py <infer-orientation-fit-pvwatts.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: infer-orientation-fit-pvwatts.ipynb <infer-orientation-fit-pvwatts.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_