PVAnalytics

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.

Library Overview

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, filtering, and features will take a series of data and return a series of booleans.

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

  • irradiance provides quality checks for irradiance measurements. This will initially contain an implementation of the QCRad algorithm, but any other quality tests for irradiance data should be added here.
  • 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.

  Other quality checks such as detecting timestamp errors will also be included in quality.

• filtering as the name implies, contains functions for data filtering (e.g. day/night or solar position)

• 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.

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

• translate contains functions for translating data to other conditions (e.g. IV curve translators, temperature adjustment, irradiance adjustment)

• metrics contains functions for computing PV system-level metrics

• fitting contains submodules for different types of models that can be fit to data (e.g. temperature models)

• dataclasses contains classes for normalizing data (e.g. an IVCurve class)