Tinkering in Iotic Space
by Mark Wharton
One of the best things about being Tech Lead at Iotic Labs is that you get the chance to tinker with some pet projects and call it “work”. I’ve had solar panels on my house for about 5 years and I’ve been itching to get them into Iotic Space.
Being a tech-tinkerer, I’ve long-since managed to get a Raspberry Pi to read their details using an open source project, SBFspot (https://github.com/sbf-/SBFspot). I spent a few hours writing a python wrapper that provides the latest values from the CSV files that the basic SBFspot tool writes.
Then came the fun part. Half an hour and a few lines of python to describe my thing and its feed to Iotic Space, such as:
t_solar_panels = client.create_thing("SolarPanels") t_solar_panels_meta.set_label("Mark's Solar Panels") t_solar_panels_meta.set_location(52.1965071, 0.6067687) t_solar_panels.create_tag(["solar", "photovoltaic"]) f_current_values = t_solar_panels.create_feed("Current Values")
and then I could read the solar values and share them:
Tada! Hmmm. So here’s my panels, sharing their data into Iotic Space for all they’re worth, but if no-one is listening, does that matter? On one level, no, because sharing into Iotic Space is like advertising something on eBay. You’re not sure if anybody will want to buy it, but there’s a pretty good chance someone will.
But my poor panels were lonely without a follower, so I wrote them one. As this is just tinkering, the one I wrote just follows the data for an hour and then plots the values on plot.ly. You can see the results here. Here’s a screen shot of how it looks in the browser.
Our friends at Energenie make some very interesting energy monitoring kits that are Raspberry Pi compatible.
See here: https://energenie4u.co.uk/catalogue/category/Raspberry-Pi-Accessories.
The next thing I’d like to do with my set-up is to maximise the use of my electric immersion heater. I want to heat my hot water when my panels are chucking out power and not to turn it on when they’re not. I’ll let the thermostat on the hot water tank sort out whether or not to actually heat the water so it doesn’t boil!
The truly amazing thing about Iotic Space is that it’s not point-to-point. My trivial plot.ly graph isn’t all that could happen. Someone else could follow my solar panels with some others to do a geographical aggregation; someone else could mash their data with weather data on sunshine levels and work out their efficiency. A power company could mash the output with the weather forecast to predict if there’s likely to be a reverse load on the grid on a sunny day.
We’ll publish the code (and a simple “follow and plot on plot.ly” in our examples section once it’s been reviewed. Then you can have a go yourself.