Learn R Programming

PVplr (version 0.1.2)

Performance Loss Rate Analysis Pipeline

Description

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 . 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.

Copy Link

Version

Install

install.packages('PVplr')

Monthly Downloads

50

Version

0.1.2

License

MIT + file LICENSE

Maintainer

Roger French

Last Published

February 14th, 2023

Functions in PVplr (0.1.2)

plr_bootstrap_uncertainty

Bootstrap: Resampling data going into each Model
plr_bootstrap_output_from_results

Bootstrap: Resample from individual Models
mbm_resample

Dataframe resample function
nc

function to convert to character then numeric
lin_inter_hrly_to_fifteen

Linearly interpolate hourly data to 15 min data.
plr_bootstrap_output

Bootstrap: Resampling from individual Models
plr_6k_model

6k Method for PLR Determination
parallel_cluster_export

Export variables to a cluster.
num_test

function to test is the values in a column should be numeric
lin_inter_missing_energy

Linearly interpolate missing energy values.
plr_pvusa_model

PVUSA Method for PLR Determination
plr_cleaning

Basic Data Cleaning
plr_build_var_list

Build a Custom Variable List
plr_remove_outliers

Filter outliers from Power Predicted Data
plr_pvheatmap

Title Heatmap generation for PV data
plr_kmeans_test

Statistical k-means Test
plr_variable_check

Define Standard Variable Names
plr_var

PLR linear model uncertainty
plr_xbx_model

XbX Method for PLR Determination
plr_weighted_regression

Weighted Regression
plr_convert_columns

Fix Column Typings
spline_timestamp_sync

Spline columns to match timestamps.
test_df

DOE RTC Sample PV System Data
plr_decomposition

Decompose Seasonality from Data
plr_seg_extract

Segmented linear PLR extraction function
plr_xbx_utc_model

UTC Method for PLR Determination
plr_yoy_regression

Year-on-Year Regression
plr_saturation_removal

Removing Saturated Data
ts_inflate

Inflate a time series data set.
time_frequency

Determines the minutes between data points in a time-series
anomalies

Fixes the anomlies
data_quality_check

checks the quality of the data after and before cleaning
day_time_start_end

finds median start and end time of PV operation
all_na

function to test if an entire column is NA
df_With_on_time

data with PV on time flag.
Int

Largest Intervals
grade_pv

returns quality information of time series data of PV
ip_num_time

Numerical time interim predictor.
anomaly_detector

detects rhw anomalies and returns a dataframw with cleaned and anom_flag column
data_structure

Reads jci files gotten in budget period 2