[This is an archived page. I am no longer an academic, having chosen an entrepreneurial route as the best path forward to positively impacting our world. I left this content online to inspire others who may come across it]

The anusolar R package

I have been planning to release an R package for over a year now, and a beta version is finally here.

Here is why you should use it, and how you can get started

Firstly, this is not a traditional R package. It's not available on cRan, the documentation is different than the accepted standard, and there aren't a whole lot of help functions quite yet.  I've done it this way for several reasons:

This is me. I like using R for science. I think you should too. (ISES Solar World Congress, 2015, Daegu, Korea)

This is me. I like using R for science. I think you should too. (ISES Solar World Congress, 2015, Daegu, Korea)

  1. In order to use the package, you need to also be able to work with data on your local machine. You need to be in control of this data, while working within an overarching data structure that makes the code robust.

  2. I've tried to get this package into a cRan ready format, but was continually frustrated by the limitations and format of the packages

  3. I need to restrict access to this, for the time being, to a select number of users, as we trial the system. I also need to have a different license structure than the R standard (more on that soon)

Regardless, there are a number of researchers working with this package already, and things are going well so far.  I am hopeful that in the future, I'll find additional development help with this package (any takers? :-D ). 


Why should you use this package?

Reason 1: The ENGERer2 separation model [Ready now]

First off, this package will enable you to calculate the Engerer 2 diffuse fraction model (separation model), which was recently ranked the best model in the world (#1 out of 140 models, using data from 54 sites), by Gueymard and Ruiz-Arias 2015:

it is concluded that ENGERER2 has the best generalization skill, and can thus be considered a ‘‘quasi-universal” 1-min separation model, wherever and whenever low-albedo conditions prevail.

Not a bad result for lowly Ph.D. student! I'm pretty excited about this result. More importantly, I am excited to enable scientists around the world to work with this model (read more about Engerer 2 here).

Reason 2: you can Simulate many different radiation models in R [COMING soon]

I've designed this package to work with open source atmospheric data (using the SoDa database), and have completed two validation studies on radiation models in Australia.  The first on separation models and the second on clear-sky radiation models (blog post here). As a result, I have all the code necessary to compute these models in R.  You might need to provide some of your own meteorological data, but I'll provide more info on that soon [Note: Not all the models are fully documented/functioning yet, hence the 'beta']

The separation models which are available (Engerer 2015)

The clear sky radiation models which are available (Engerer and Mills 2015)

REASON 3: You can simulate PV systems and calculate KPV [COMING SOON]

Yeah, you know KPV, that clear sky index for PV that I simply won't stop talking about (shut up already Nick!).  With this R package, it is very straightforward to simulate the clear sky power output of a given PV system, meaning that you can then normalise the power output from that PV system.  Doing this allows you to make KPV based estimates of nearby, unmonitored PV system power output which is the main engine in the Regional PV Simulation System (which I use to simulate tens of thousands of PV systems).  It is also great for pre-processing PV data time-series before feeding it to a machine-learning/statistical forecast algorithm.  Simulating PV systems is handy for other reasons too, like using input data from a pyranometer to simulate PV system power output like I did in my M.S. Thesis.


I have been working in the field of solar forecasting for the past 4 years, and have a lot of experience in this area.  One of the things I want to use this package for, is to make it very easy for researchers to make some basic solar forecasts in their region.  First, I'll make it easy to extract surface radiation forecasts from the GFS.  Next, I'll show you how to use a Support Vector Machine (SVM) to produce a solar forecasting from historical power output data. 

REASON 5: I want to incorporate your research methods into this package, to increase your influence and raise your citation count

Plain and simple: if it is easy to use your methods, then more people will do so, that means more citations and more influence in the scientific community.  Having your method for extracting solar radiation from satellite imagery, completing a time-series analysis on variability, or extracting a radiation estimate from PV system power output (whatever it may be) as part of this package is exactly what I want.  I'm particularly interested in partnering with young researchers, early in their career (Honours, Ph.D, Postdocs), as you guys are the most open to collaborating!  (All the old folks get caught up in their own work and 'competing' with other scientists - we're all on team solar are we not??). 

REady to try it out?

If you're interested, follow through the below modules, which will help you get started.  This "R Manual" is under construction, so bear with me, while it grows.  And please, provide feedback on anything that is unclear using the comment boxes at the bottom of each page.

Follow the below links for the rpackage manual