PVAnalytics is a python library that supports analytics for PV systems. It provides functions for quality control, filtering, and feature labeling and other tools supporting the analysis of PV system-level data.

PVAnalytics is available at PyPI and can be installed using pip:

pip install pvanalytics

Documentation and example usage is available at pvanalytics.readthedocs.io.

The functions provided by PVAnalytics are organized in modules based on their anticipated use. The structure/organization below is likely to change as use cases are identified and refined and as package content evolves. The functions in quality and features take a series of data and return a series of booleans. For more detailed descriptions, see our API Reference.

• quality contains submodules for different kinds of data quality checks.

  • data_shifts contains quality checks for detecting and isolating data shifts in PV time series data.
  • irradiance provides quality checks for irradiance measurements.
  • weather has quality checks for weather data (for example tests for physically plausible values of temperature, wind speed, humidity, etc.)
  • outliers contains different functions for identifying outliers in the data.
  • gaps contains functions for identifying gaps in the data (i.e. missing values, stuck values, and interpolation).
  • time quality checks related to time (e.g. timestamp spacing)
  • util general purpose quality functions.

• features contains submodules with different methods for identifying and labeling salient features.

  • clipping functions for labeling inverter clipping.
  • clearsky functions for identifying periods of clear sky conditions.
  • daytime functions for for identifying periods of day and night.
  • orientation functions for labeling data as corresponding to a rotating solar tracker or a fixed tilt structure.
  • shading functions for identifying shadows.

• system identification of PV system characteristics from data (e.g. nameplate power, orientation, azimuth)

• metrics contains functions for computing PV system-level metrics