grade_pv

returns quality information of time series data of PV

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.

Roger French

PVplr

Performance Loss Rate Analysis Pipeline

Alan Curran

Tyler Burleyson

William Oltjen

Sascha Lindig

David Moser

Solar Durability and Lifetime Extension research center 

grade_pv function

<dl><dt>df</dt>
<dd>the PV time series data. It can be the direct output of read.csv(file_name, stringsAsFactors = F)</dd>
<dt>col</dt>
<dd>column of the grading, default 'poay'</dd>
<dt>id</dt>
<dd>The name of the pv data</dd>
<dt>timestamp_col</dt>
<dd>the character name of the timestamp column</dd>
<dt>timestamp_format</dt>
<dd>the POSIXct format of the timestamp if conversion is needed</dd>
<dt>batch_days</dt>
<dd>the batch of data that the anomaly detection is applied. Since time series decomposition is used,
one seasonality will be applied for whole data which is inefficeint, if NA, will pass whole</dd></dl>

Arguments

Author

returns quality information of time series data of PV — grade_pv

<dl>

<dt>df</dt>
<dd>the PV time series data. It can be the direct output of read.csv(file_name, stringsAsFactors = F)</dd>


<dt>col</dt>
<dd>column of the grading, default 'poay'</dd>


<dt>id</dt>
<dd>The name of the pv data</dd>


<dt>timestamp_col</dt>
<dd>the character name of the timestamp column</dd>


<dt>timestamp_format</dt>
<dd>the POSIXct format of the timestamp if conversion is needed</dd>


<dt>batch_days</dt>
<dd>the batch of data that the anomaly detection is applied. Since time series decomposition is used,
one seasonality will be applied for whole data which is inefficeint, if NA, will pass whole</dd>

</dl>

grade_pv: returns quality information of time series data of PV

Description

Usage

Arguments

Author