From b5666e796386670991320a344e966e8517bba756 Mon Sep 17 00:00:00 2001 From: Michael Deceglie Date: Thu, 24 Oct 2024 18:05:15 -0600 Subject: [PATCH] Doc updates --- docs/sphinx/source/changelog/pending.rst | 2 + docs/sphinx/source/conf.py | 2 +- docs/sphinx/source/index.rst | 76 +++--------------------- 3 files changed, 12 insertions(+), 68 deletions(-) diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst index 2d4adc83..e7d2a316 100644 --- a/docs/sphinx/source/changelog/pending.rst +++ b/docs/sphinx/source/changelog/pending.rst @@ -184,3 +184,5 @@ Contributors * Martin Springer (:ghuser:`martin-springer`) * Michael Deceglie (:ghuser:`mdeceglie`) * Kirsten Perry (:ghuser:`kperrynrel`) +* Dirk Jordan (:ghuser:`dirkjordan`) +* Chris Deline (:ghuser:`cdeline`) diff --git a/docs/sphinx/source/conf.py b/docs/sphinx/source/conf.py index 72889388..c46717ae 100644 --- a/docs/sphinx/source/conf.py +++ b/docs/sphinx/source/conf.py @@ -22,7 +22,7 @@ # -- Project information ----------------------------------------------------- project = 'RdTools' -copyright = '2016–2021 kWh Analytics, Alliance for Sustainable Energy, LLC, SunPower, and DNV' +copyright = '2016–2024 kWh Analytics, Alliance for Sustainable Energy, LLC, SunPower, and DNV' author = 'kWh Analytics, Alliance for Sustainable Energy, LLC, SunPower, and DNV' # The full version, including alpha/beta/rc tags diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst index 9e462c5b..92fc8cf9 100644 --- a/docs/sphinx/source/index.rst +++ b/docs/sphinx/source/index.rst @@ -53,7 +53,12 @@ The preferred method for degradation rate estimation is the year-on-year (YOY) approach (Jordan 2018), available in :py:func:`.degradation.degradation_year_on_year`. The YOY calculation yields in a distribution of degradation rates, the central tendency of which is the most representative of the true -degradation. The width of the distribution provides information about +degradation. We note that the workflow described above and implimented in +:py:class:`.analysis_chains.TrendAnalysis` provides an estimate of degradation rate, +not "performance loss rate" (PLR). PLR includes losses that are explicitly filtered +out by the primary workflow (Deceglie 2023). + +The width of the distribution provides information about the uncertainty in the estimate via a bootstrap calculation. The :ref:`examples` use the output of :py:func:`.degradation.degradation_year_on_year` to visualize the calculation. @@ -160,70 +165,6 @@ environment by first installing RdTools as described above, then running ``pip install -r docs/notebook_requirements.txt`` from the base directory. -The following functions are used for degradation and soiling analysis: - -.. code:: python - - import rdtools - -The most frequently used functions are: - -.. code:: python - - normalization.normalize_with_expected_power(pv, power_expected, poa_global, - pv_input='power') - ''' - Inputs: Pandas time series of raw power or energy, expected power, and - plane of array irradiance. - Outputs: Pandas time series of normalized energy and POA insolation - ''' - -.. code:: python - - filtering.poa_filter(poa_global); filtering.tcell_filter(temperature_cell); - filtering.clip_filter(power_ac); filtering.logic_clip_filter(power_ac); - filtering.xgboost_clip_filter(power_ac); filtering.normalized_filter(energy_normalized); - filtering.csi_filter(poa_global_measured, poa_global_clearsky); - ''' - Inputs: Pandas time series of raw data to be filtered. - Output: Boolean mask where `True` indicates acceptable data - ''' - -.. code:: python - - aggregation.aggregation_insol(energy_normalized, insolation, frequency='D') - ''' - Inputs: Normalized energy and insolation - Output: Aggregated data, weighted by the insolation. - ''' - -.. code:: python - - degradation.degradation_year_on_year(energy_normalized) - ''' - Inputs: Aggregated, normalized, filtered time series data - Outputs: Tuple: `yoy_rd`: Degradation rate - `yoy_ci`: Confidence interval `yoy_info`: associated analysis data - ''' - -.. code:: python - - soiling.soiling_srr(energy_normalized_daily, insolation_daily) - ''' - Inputs: Daily aggregated, normalized, filtered time series data for normalized performance and insolation - Outputs: Tuple: `sr`: Insolation-weighted soiling ratio - `sr_ci`: Confidence interval `soiling_info`: associated analysis data - ''' - -.. code:: python - - availability.AvailabilityAnalysis(power_system, power_subsystem, - energy_cumulative, power_expected) - ''' - Inputs: Pandas time series system and subsystem power and energy data - Outputs: DataFrame of production loss and availability metrics - ''' - Documentation ------------- @@ -237,7 +178,6 @@ take one of several types, we document them using the type alises listed below: ``numeric`` scalar or ``pandas.Series``. Typically int or float dtype. - Citing RdTools -------------- To cite RdTools, please use the following along with the version number @@ -273,7 +213,6 @@ appropriate: Detection Techniques in AC Power Time Series," 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), 2021, pp. 1638-1643, DOI: `10.1109/PVSC43889.2021.9518733 `_ - References ---------- @@ -292,6 +231,9 @@ methodology include: methodology comparison — A basis for a standard", in 43rd IEEE Photovoltaic Specialists Conference, Portland, OR, USA, 2016, DOI: 10.1109/PVSC.2016.7749593. +- M. G. Deceglie, K. Anderson, D. Fregosi, W.B. Hobbs, M.A. Mikofski, + M. Theristis, and B. E. Meyers, "Perspective: Performance Loss Rate in + Photovoltaic Systems", Sol. RRL, 7: 2300196. DOI: 10.1002/solr.202300196 - Jordan DC, Kurtz SR, VanSant KT, Newmiller J, Compendium of Photovoltaic Degradation Rates, Progress in Photovoltaics: Research and Application, 2016, 24(7), 978 - 989.