It’s been a while since I posted as I’ve been focussed on finishing the PhD thesis, which is now submitted. In the coming months I’ll be trying to summarise the work here and keep current research more up to date. In the meantime I’ll document some of the posters I’ve presented at workshops over the last few years.
This extends a previous post on anthropogenic waste heat to include plots for New York and London.
Writing an article in LaTeX produces crisp, quality documents and beautiful equations. However, it’s not very user friendly. Recently I’ve been using another language, Markdown, because I find it more readable and intuitive, but it still has LaTeX equation support. To me that’s the best of both worlds. So how about writing an entire PhD thesis in Markdown? This post shows you how.
I’ve recently finished developing a computational model which predicts the heating and cooling energy demands of a neighbourhood based on building characteristics, meteorological conditions and the behaviour of people. However, I don’t have a perfect and complete set of observations to describe the system, and I am finding it difficult to find appropriate values for some parameters. So I’ve used machine learning to help.
Cities can be much hotter than surrounding regions. This is in part because of waste heat from our energy use. This post shows how much waste heat is emitted during a typical day into Melbourne, Australia.
This post describes how our interface conduction scheme is formulated and computed, and finishes with Fortran code which solves various approximations of the heat equation.
I’m new to building websites, so didn’t know where to begin.
I knew I wanted a simple end product that I had full control over at the lowest possible cost, and that I was comfortable with command line interfaces.
Here is where I ended up.
Our cities are getting hotter. We can do something about it, but it isn’t a case of one-size-fits-all.
