Late last summer, I decided to build a cold frame to be able to continue gardening and harvesting home-grown food into the winter. It was a big undertaking for me to buy the cedar boards and assemble them effectively along with the polycarbonate lid, but I really enjoyed the process!

The wildfires that raged through Colorado in the summer of 2020 both horrified and fascinated me. I volunteered to help with the local fire response one day as a way to better understand how the fires are fought. I also made a data visualization to help satisfy my curiosity about how the fire progressed. This blog summarizes the data used and the decisions made to generate an effective and intuitive animation of the Cameron Peak fire.

During my time on the bench at work in the summer of 2019, I spent a lot of time teaching myself the principles of reinforcement learning and distilling them down into a one hour talk. You can find the slides for my presentation here. This presentation assumes a basic knowledge of neural networks.

At long last, I am publishing a blog on my longest running side project to date - a web application built usng R’s Shiny that allows anyone with multiple amortized loans such as student loans or mortgages to determine exactly how much time and money they could save by making overpayments. I describe how the application works, the technical challenges encountered, and a discussion of the implementation details, including code documentation.

For this blog you are reading now, I decided to go with Jekyll and to use .md files. This was very easy as I only had to fork an already organized repo from barryclark/jekyll-now that had a theme that I liked. However, at work I wanted to set up a blog using RStudio’s blogdown which uses Hugo, even though the upfront setup time takes longer. I was particularly interested in this methodology because it allowed me to simply convert my R Notebooks into blog posts, without having to save (and probably re-save plots) and refer to the location of the png files.

This blog post describes the methodology to estimate solar power generation by all controlled premises with solar panels within a specific utility. Using this utility’s latitude and longitude, along with date and time, we can obtain reasonable forecasts of clear sky GHI, a measure of solar irradiance. In conjunction with cloud cover and the number of controlled premises with solar systems, we can use the following formula to create an estimate of solar generation: