I'm attending the SciPy conference this year with two main objectives
- Look for best practices to integrate into my research on energy access
- Look for best practices to integrate into my teaching
I'm interested in the development of open-source solutions in the small-scale energy analysis space. There are free tools like PVWatts and paid tools like HOMER but in my experience (please let me know otherwise) the source code is not available. I've also seen some economy-scale tools like TEMOA. I'm interested in the creation of tools for the calculation of the solar photovoltaic resource that are extensible and open. This means taking special care in the creation of the API and architecture. I'm also interested in the readability of the code so that it can be approached by new contributors.
Many of my students are very interested in careers that will influence our energy systems. They are often dismayed when they learn that quantitative skills play a large part in these careers. A second difficulty students deal with is the communication of quantitative results. I'm interested in how the tools in scientific computing can be used to address both of these problems. Students currently use a combination of ink, paper, and a hand-held calculator to arrive at estimations and communicate results. This creates many opportunities for transcription or computational errors that cannot be diagnosed. Now that many free tools like Sage or IPython Notebook can approach the ease of use of Mathematica or Mathcad without licensing headaches, I'm eager to try them in the classroom.