Package: PVplr 0.1.2

Roger French

PVplr: Performance Loss Rate Analysis Pipeline

The pipeline contained in this package provides tools used in the Solar Durability and Lifetime Extension Center (SDLE) for the analysis of Performance Loss Rates (PLR) in real world photovoltaic systems. Functions included allow for data cleaning, feature correction, power predictive modeling, PLR determination, and uncertainty bootstrapping through various methods <doi:10.1109/PVSC40753.2019.8980928>. The vignette "Pipeline Walkthrough" gives an explicit run through of typical package usage. This material is based upon work supported by the U.S Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE-0008172. This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University.

Authors:Alan Curran [aut], Tyler Burleyson [aut], William Oltjen [aut], Sascha Lindig [aut], David Moser [aut], Roger French [aut, cre], Solar Durability and Lifetime Extension research center [cph, fnd]

PVplr_0.1.2.tar.gz
PVplr_0.1.2.zip(r-4.5)PVplr_0.1.2.zip(r-4.4)PVplr_0.1.2.zip(r-4.3)
PVplr_0.1.2.tgz(r-4.4-any)PVplr_0.1.2.tgz(r-4.3-any)
PVplr_0.1.2.tar.gz(r-4.5-noble)PVplr_0.1.2.tar.gz(r-4.4-noble)
PVplr_0.1.2.tgz(r-4.4-emscripten)PVplr_0.1.2.tgz(r-4.3-emscripten)
PVplr.pdf |PVplr.html✨
PVplr/json (API)

# Install 'PVplr' in R:
install.packages('PVplr', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Datasets:
  • test_df - DOE RTC Sample PV System Data

On CRAN:

archivedpackagesr-universe

3.53 score 5 stars 17 scripts 190 downloads 31 exports 59 dependencies

Last updated 9 days agofrom:e7422efd71 (on package/PVplr). Checks:7 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKJan 12 2025
R-4.5-winOKJan 12 2025
R-4.5-linuxOKJan 12 2025
R-4.4-winOKJan 12 2025
R-4.4-macOKJan 12 2025
R-4.3-winOKJan 12 2025
R-4.3-macOKJan 12 2025

Exports:all_naanomaly_detectordata_quality_checkdata_structuregrade_pvIntmbm_resamplencnum_testparallel_cluster_exportplr_6k_modelplr_bootstrap_outputplr_bootstrap_output_from_resultsplr_bootstrap_uncertaintyplr_build_var_listplr_cleaningplr_convert_columnsplr_decompositionplr_pvheatmapplr_pvusa_modelplr_remove_outliersplr_saturation_removalplr_seg_extractplr_varplr_variable_checkplr_weighted_regressionplr_xbx_modelplr_xbx_utc_modelplr_yoy_regressionspline_timestamp_synctime_frequency

Dependencies:backportsbroomcliclustercolorspacecpp11curldplyrfansifarverforecastfracdiffgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvminpack.lmmunsellnlmennetpillarpkgconfigpurrrquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangscalessegmentedstlplusstringistringrtibbletidyrtidyselecttimeDatetseriesTTRurcautf8vctrsviridisLitewithrxtsyaImputezoo

Feature Correction

Rendered fromfeature_correction.Rmdusingknitr::rmarkdownon Jan 12 2025.

Last update: 2025-01-11
Started: 2025-01-11

Power Predictive Model Comparison

Tyler Burleyson

Rendered frommodel_comparison.Rmdusingknitr::rmarkdownon Jan 12 2025.

Last update: 2025-01-11
Started: 2025-01-11

Evaluation of Non-Linear PLR

Alan Curran

Rendered fromnonlinear_plr.Rmdusingknitr::rmarkdownon Jan 12 2025.

Last update: 2025-01-11
Started: 2025-01-11

Pipeline Walkthrough

Tyler Burleyson

Rendered frompipeline_walkthrough.Rmdusingknitr::rmarkdownon Jan 12 2025.

Last update: 2025-01-11
Started: 2025-01-11

Readme and manuals

Help Manual

Help pageTopics
function to test if an entire column is NAall_na
Fixes the anomliesanomalies
detects rhw anomalies and returns a dataframw with cleaned and anom_flag columnanomaly_detector
checks the quality of the data after and before cleaningdata_quality_check
Reads jci files gotten in budget period 2data_structure
finds median start and end time of PV operationday_time_start_end
data with PV on time flag.df_With_on_time
returns quality information of time series data of PVgrade_pv
Largest IntervalsInt
Numerical time interim predictor.ip_num_time
Linearly interpolate hourly data to 15 min data.lin_inter_hrly_to_fifteen
Linearly interpolate missing energy values.lin_inter_missing_energy
Dataframe resample functionmbm_resample
function to convert to character then numericnc
function to test is the values in a column should be numericnum_test
Export variables to a cluster.parallel_cluster_export
6k Method for PLR Determinationplr_6k_model
Bootstrap: Resampling from individual Modelsplr_bootstrap_output
Bootstrap: Resample from individual Modelsplr_bootstrap_output_from_results
Bootstrap: Resampling data going into each Modelplr_bootstrap_uncertainty
Build a Custom Variable Listplr_build_var_list
Basic Data Cleaningplr_cleaning
Fix Column Typingsplr_convert_columns
Decompose Seasonality from Dataplr_decomposition
Statistical k-means Testplr_kmeans_test
Title Heatmap generation for PV dataplr_pvheatmap
PVUSA Method for PLR Determinationplr_pvusa_model
Filter outliers from Power Predicted Dataplr_remove_outliers
Removing Saturated Dataplr_saturation_removal
Segmented linear PLR extraction functionplr_seg_extract
PLR linear model uncertaintyplr_var
Define Standard Variable Namesplr_variable_check
Weighted Regressionplr_weighted_regression
XbX Method for PLR Determinationplr_xbx_model
UTC Method for PLR Determinationplr_xbx_utc_model
Year-on-Year Regressionplr_yoy_regression
Spline columns to match timestamps.spline_timestamp_sync
DOE RTC Sample PV System Datatest_df
Determines the minutes between data points in a time-seriestime_frequency
Inflate a time series data set.ts_inflate