What methods do people use to capture the data? I saw the github project that has a script to do it, at least I think that’s what it does. But another method that I used was to use the Burp Proxy tool (free!) and run all the monitoring traffic through that and see the json data in the websockets window. The data updates every 1-2 seconds, so it’s a lot.
I’m pretty new to having solar panels (about 2 weeks) so I’m looking for some cool analysis ideas!
I run a modified version of the github python code and capture the sense data every 15 seconds. I write it to InfluxDB and look at it with Grafana. All free running in Docker containers on my NAS.
That’s really well done @ben3 (tagged you to get another badge!)
I see in the online Sense graphs that I can see my usage, and I can see my solar generation, and there are places that I can see them side by side. I think I might want to create something more fine-grained using these data points. I might also use this extra data that I’m discovering to self-identify more of my “Other” devices in the house.
I’ll take a look at Grafana, I haven’t heard of that one before, or InfluxDB. I just did a quick lookup and saw they are built for exactly this type of purpose, so that’s pretty awesome. Thanks!
@samheidie: I can’t answer that exactly because I record a BUNCH of data. I started this project over XMas break so my data starts Jan 1. Total storage in use by InfluxDB is 67M. I’m pulling weather data from several sources, HVAC states from 4 systems, etc.
Cool, thanks! I was also trying to think of how I could get better real-time gas data. The provider sends a monthly update, but would love to get something that tracks similar to what Sense does.
I made the decision not to try to store/retain my own data, partially because many of my sources, my power utility, my thermostats, Sense and my solar provider all supply hourly export (or even finer granularity). When I do want to see at tighter resolution, I do use the APIs, plus @duanetiemann’s utility for HS110’s.
And just for a little fun, the existing Sense Power Meter waveforms can be harvested from the web app for limited analysis, like this…
Essentially, what’s happening is that my meter broadcasts a very low power RF signal, un-encrypted, with a small data packet that contains the meter ID and the current reading. This USB dongle is actually a radio receiver. RTL-SDR is software defined radio using the RTL2832U chipset. See here: About RTL-SDR. A small app runs in the background that controls the RTL chipset and serves the data via TCP. RTLAMR is a small Go app that connects to the RTL-TCP app and receives the data and serves it up to my Python script that writes it to InfluxDB. My to-do list has a line to rewrite all of this and simplify the flow of data, but for now this is working.
We do, but I’m not seeing the data in the default scan range of the RTL. When it warms up I’m going to take the ground cover off my water meter pit (it’s in the front yard) and get the model number and figure out how to track that as well.
The default range for RTLAMR worked for my meter, so I didn’t experiment farther. There is a parameter to RTL-TCP that controls the freq range. Best thing would be to google your meter’s model number and see what it uses.