I'll second that. I work in the renewable energy space where we get all manner of atmospheric and power data in a wide variety of units depending on the data source, so Pint is incredibly useful in normalizing them as well as making it clear in the code what and how unit conversion is happening. The fact that it integrates fairly nicely with Pandas and Numpy is great, too.
Unrelated question. I was recently appointed to a citizens energy commission and I'm finding my self modeling energy use and generation in Python. I'm curious if you would suggest any models of residential and commercial energy consumption and renewable energy generation. I'm currently generating a lot of my own from scratch and I'm being (rightly) criticized by my colleagues to use something off the shelf.
If you're looking for something turn-keyish, you might be able to get some help with this from one or more of the big players in the renewable energy analysis and forecasting space, including (in no particular order) DNV, Vaisala, Bloomberg, and Ventyx. Beware, their offerings can get pretty expensive and there's often no standard list pricing. Also, I strongly encourage you to always do your own validation of any model data they might give you, so the work you're doing to produce your own models is probably not wasted.
