Timeseries Guide

Module Overview

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 Hopwood and Gunda [HG22] 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 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>.

Preprocess

• pvops.timeseries.preprocess.prod_inverter_clipping_filter() filters out production periods with inverter clipping. The core method was adopted from pvlib/pvanalytics.

• pvops.timeseries.preprocess.normalize_production_by_capacity() normalizes power by site capacity.

• 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.

• pvops.timeseries.preprocess.establish_solar_loc() adds solar position data to production data using pvLib.

Models

• 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.

• pvops.timeseries.models.AIT.AIT_calc() Calculates expected energy using measured irradiance based on trained regression model from field data.

• pvops.timeseries.models.iec.iec_calc() calculates expected energy using measured irradiance based on IEC calculations.

Example Code

load in data and run some processing functions