These funcions provide processing and modelling capabilities for timeseries production data. Processing functions prepare data to train two types of expected energy models:
- AIT: additive interaction trained model, see :cite:t:`app12041872` for more information.
- Linear: a high flexibility linear regression model.
Additionally, the ability to generate expected energy via IEC standards (iec 61724-1) is implemented in the :py:mod:`~pvops.timeseries.models.iec` module.
An example of usage can be found in tutorial_timeseries_module.ipynb <https://github.com/sandialabs/pvOps/blob/master/tutorials/tutorial_timeseries_module.ipynb>.
- :py:func:`pvops.timeseries.preprocess.prod_inverter_clipping_filter` filters out production periods with inverter clipping. The core method was adopted from pvlib/pvanalytics.
- :py:func:`pvops.timeseries.preprocess.normalize_production_by_capacity` normalizes power by site capacity.
- :py:func:`pvops.timeseries.preprocess.prod_irradiance_filter` filters rows of production data frame according to performance and data quality. NOTE: this method is currently in development.
- :py:func:`pvops.timeseries.preprocess.establish_solar_loc` adds solar position data to production data using pvLib.
- :py:func:`pvops.timeseries.models.linear.modeller` is a wrapper method used to model timeseries data using a linear model. This method gives multiple options for the learned model structure.
- :py:func:`pvops.timeseries.models.AIT.AIT_calc` Calculates expected energy using measured irradiance based on trained regression model from field data.
- :py:func:`pvops.timeseries.models.iec.iec_calc` calculates expected energy using measured irradiance based on IEC calculations.
load in data and run some processing functions