Chacmool is the name of my new weather project. This is to keep me off the streets since I'm not doing an Open University degree anymore. The idea (long term) is to make automated weather observations and use "artificial intelligence" to try and predict the weather.
This project will live in the garden shed, run on solar power and be controlled using a Raspberry Pi computer.
I've already done a lot of the fun stuff, i.e., buying various parts off Ebay and Aliexpress but I intend to keep some sort of log of my progress from here on in.
Why is it called Chacmool?
I saw a chacmool statue in Merida, Mexico a few years ago and thought it was sort of cool. And according to Wikipedia, "Aztec chacmools bore water imagery and were associated with Tlaloc, the rain god." That's all the excuse I need.
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)
Why an AI weather station?
I've read "Make Your Own Neural Network" by Tariq Rashid and it was an excellent book - I'd recommend it to anyone who wants to know how things like 'deep learning' (or whatever it's called this year) work. That got me idly wondering what I could use a neural network for, other than reading hand-written digits (the classic 'hello world' application of AI). On the hardware side, I'd already got some bits from an abandoned shed-based project to use a Raspberry Pi for "off-site backup". That gave me the idea then that I could build a shed-bound neural network and feed it with sheddy weather data - and see what happens...