{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"PVAnalytics provides quality control, filtering, feature labeling, and analysis functions for photovoltaic system-level data, organized into modules for data quality checks, feature identification, system characterization, and metrics computation.","skillfed_tags":["solar-energy","time-series-analysis","data-quality"],"use_cases":["Detect and isolate data shifts or anomalies in PV time series before feeding data into analysis pipelines.","Identify periods of inverter clipping to assess system performance limitations and capacity factors.","Label clear sky conditions and day/night periods for benchmarking PV system performance against models.","Validate irradiance and weather measurements for physical plausibility before using in system analysis.","Extract system characteristics like nameplate power and orientation from operational data.","Compute system-level metrics and KPIs for performance monitoring and reporting."],"what_it_does":"PVAnalytics is a Python library for analyzing photovoltaic system-level data. It organizes analytics functions into modules for quality control (data shifts, irradiance, weather, outliers, gaps, time checks), feature identification (inverter clipping, clear sky periods, day/night, tracker orientation, shading), system characterization, and metrics computation. The library returns boolean series for quality and feature functions, making it easy to filter or label PV time series data.\n\nThe package targets researchers and engineers working with solar data, building on established dependencies like numpy, pandas, scipy, and pvlib. It's designed to handle the specific data quality and feature extraction challenges common in PV system analysis, from detecting measurement anomalies to identifying operational modes like inverter clipping or clear sky conditions.","worth_installing":"Yes. PVAnalytics is actively maintained, has low install friction, carries no known vulnerabilities, and offers a focused toolkit for a specific domain (PV analytics). The MIT license is permissive. Install it if you work with photovoltaic system data and need quality control, feature labeling, or system characterization functions; the modular design lets you use only what you need."},"id":"pvanalytics","links":{"html":"https://skillfed.io/packages/pvanalytics","md":"https://skillfed.io/packages/pvanalytics.md","pypi":"https://pypi.org/project/pvanalytics/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-11-27","license_spdx":null,"license_treatment":"permissive","name":"pvanalytics","python_support":"unspecified","summary":"PVAnalytics is a python library for the analysis of photovoltaic system-level data."},"popularity":{"monthly_downloads":92267,"position":13463,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.2"}