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Update overview #202

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Sep 23, 2020
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Update overview #202

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24 changes: 13 additions & 11 deletions docs/sphinx/source/index.rst
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Expand Up @@ -17,7 +17,8 @@ time series data from photovoltaic energy systems. The library aims to provide
best practice analysis routines along with the building blocks for users to
tailor their own analyses.
Current applications include the evaluation of PV production over several years to obtain
rates of performance degradation and soiling loss. RdTools can handle
rates of performance degradation and soiling loss. They also include the capability to
analyze systems for system- and subsystem-level availability. RdTools can handle
both high frequency (hourly or better) or low frequency (daily, weekly,
etc.) datasets. Best results are obtained with higher frequency data.

Expand All @@ -27,11 +28,12 @@ Full examples are worked out in the example notebooks in the
To report issues, contribute code, or suggest improvements to this
documentation, visit the RdTools development repository on `github`_.

Workflow
--------
Degradation and Soiling
-----------------------

RdTools supports a number of workflows, but a typical analysis follows
the following:
Both degradation and soiling analyses are based on normalized yield, similar to performance
index. Usually, this is computed at the daily level although other aggregation periods are
supported. A typical analysis of soiling and degradation contains the following:

0. Import and preliminary calculations
1. Normalize data using a performance metric
Expand All @@ -47,8 +49,8 @@ drift.
.. image:: _images/RdTools_workflows.png
:alt: RdTools workflow diagram

Degradation Results
-------------------
Degradation
^^^^^^^^^^^

The preferred method for degradation rate estimation is the year-on-year
(YOY) approach, available in :py:func:`.degradation.degradation_year_on_year`.
Expand Down Expand Up @@ -77,8 +79,8 @@ analysis when details such as filtering are changed. We generally recommend
that the clear-sky analysis be used as a check on the sensor-based results,
rather than as a stand-alone analysis.

Soiling Results
---------------
Soiling
^^^^^^^

Soiling can be estimated with the stochastic rate and recovery (SRR)
method (Deceglie 2018). This method works well when soiling patterns
Expand All @@ -96,8 +98,8 @@ identified soiling rates for the dataset.
:width: 320
:height: 216

Availability Results
--------------------
Availability
------------

Evaluating system availability can be confounded by data loss from interrupted
datalogger or system communications. RdTools implements two methods of
Expand Down