Plots of yearly average temperatures in 10 cities, along with how 2018 ranked.
Next week I’ll be at the annual workshop for ARC Centre of Excellence for Climate Extremes (CLEX) group. The workshop is a chance to hear about the work researchers have been doing over the last 12 months or so, and discuss future research goals.
Below is the poster I’ll be presenting. The images in this PDF version are higher quality.
This post is a summary of our latest paper  on improving an urban climate model to better predict building energy consumption depending on local weather conditions, the structure of buildings and human behaviours.
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.
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.
In a new paper1, we introduce a method to simulate how heat is conducted through roofs, walls and roads. We show it improves the simulation of heat storage and release, a very important process in urban climate.