Yeah I know – the title, right? “What the faq is he doing with a webcam if he has a smart meter?” A contemporary UK “smart meter” is very smart: it holds your data hostage. What I had installed recently is an electronic meter with communications adapter and a little head unit to show me near-instantaneous consumption data. As soon as the meter was fitted, I went looking for the actual data that must exist at least temporarily to allow the head unit to work. The executive summary: the data is not accessible to the homeowner.
Buy a Software Defined Radio for fun, but not this
I didn’t go looking for the data straight away, I had a quick look at the head unit and the meter to see if there was anyway of connecting to them or exporting the data. There are none. “The meter must be communicating with the head unit somehow, maybe I can sniff / connect the same way?” I thought, and read several articles suggesting ‘Software Defined Radio’ would give me access to not only my meter’s data, but those of similar meters in my neighbourhood. This is either not a thing in the UK or was only briefly a thing with early models of even-less-smart meter, broadcasting your data in an easily accessible way being incompatible with privacy.
The smart meter
The standards to which UK smart meters must adhere pretty much preclude any easy tapping or sniffing of data. In fact, it appears the only way to gain access to your own smart meter data is to arrange with your supplier to send the data to them or to a 3rd party selected by your supplier, so they may offer you your own data ‘as a service’. This is a miserable shortcoming of the smart meter standard, which should be rectified as soon as possible. It must be possible for a reasonably competent homeowner to gain direct and private access to their own consumption data.
That little head unit is okay for instantaneous power and a running total, but when it gets to £20, where did the money go? There are no clues. This falls far short of any definition of ‘smart’. I believe it should be illegal to call a device ‘smart’ unless its smartness is self-contained. If you have a device that can deliver its “smart” behaviour while disconnected from the Internet, that’s a smart device. If it needs to communicate with a service hosted elsewhere, the device itself shouldn’t be marketed as anything other than a proxy, agent or facade.
The government’s publications for smart meters seem at first glance to have the intent of giving the homeowner control over their data. Where they fall short is in describing only how the homeowner grants access to their data to suppliers and third parties: there’s nothing to describe making the data accessible to the homeowner. In the government’s standards for smart meters, the best you’ll find is a circular buffer “capable of storing four hours of UTC date and time stamped six minute Consumption data”. When you can see your head unit updating the ‘live’ consumption every few seconds and billing periods are rarely less than 1 month, 40 records is effin’ risible! Thanks gov!
Where the webcam comes in
Previously, I’d read a meter with a webcam by looking at an old-school meter’s analogue dials and ‘recognising’ the digits. This time there was something a bit more ‘digital’ accessible to the webcam: a blinking red light. Next to the light is a legend that says “1,000 impulses per kWh”. To my pedantic mind, that looks like the light flashes every time one Wh (Watt-hour) is consumed, or 3.6kWs (kilowatt-seconds).
The average power consumption over the interval from one flash to the next is 3.6kWs divided by the duration of the interval. For example, if the light flashes once per second, the average power consumption is 3.6kW (3.6kWs / 1s). Another example – if the light flashes 1,000 times over an hour (the legend’s “1kWh” over an hour) the interval is 3.6s and the average power is 1kW. If you fire up an 11kW ‘power shower’ you’d expect to see about 3 blinks per second, and with everything off at night time, you’d expect some quite long delays between blinks.
The basic scheme is to take a picture of the meter with the webcam, crop out everything but the square holding the light, get an average colour for that square and (waves hands) detect when the light switches from off to on. The interval between off-on transitions can then be used to compute average power over the interval. The equipment is same as last time: my venerable Dell Mini 9 running Ubuntu 18.04 and a cheap-and-cheerful Logitech C250 webcam.
For a first shot I used uvccapture and ImageMagick from a shell script to generate a stream of RGB values for the average pixel color, then wrote a little utility that converted these to a datetime and average power pair. This worked, but not well: the capture rate was 3-5 frames per second. Even this was useful though – it was obvious from the first few hours’ data that “Child number one” was taking a half hour power shower compared to just a few minutes for everyone else. I’ve lost that data, so there’s no real “steaming gun” in the data that follows.
Here’s a reasonably representative 24 hours collected with a little Java program (attached below) driving the webcam at 50 frames per second and logging timestamps and power:
You can see from most of the night that there’s a baseline around 50W – I suspect that’s mostly low-power devices we don’t turn off overnight – our Internet router and other devices with electronic displays. The nearly-1kW ‘pulses’ between midnight and 7am are from an electric underfloor heating circuit. It’s interesting to see it spend about twice as much time off as on, so its average power is somewhere around 300W. The UFH is turned off in the morning. Some spikes visible through the day are kitchen devices being used at the same time. The kettle is around 2kW, there’s a 950W microwave, a toaster around 1kW – none of which run for very long.
Parent number one was off-shift that day, so the slightly increased baseline through the afternoon could have involved Netflix. Children numbers one and two arrive home from school either side of sunset in the late afternoon, so there’s a bit more power being consumed by lights, anime and Minecraft. Cooking / tidying up around dinner results in some 2.5kW pulses from an ‘instant’ hot water tap on the kitchen sink which has a small tank of its own. The big spikes late in the evening are from a ‘power shower’ which seems to be about 5kW. They’re mixed in with some added electric heating in the bathroom.
There’s also a regular ‘pock’ of about 300-400W which becomes more obvious around the evening ablutions (and on damp days!). I believe this is the dehumidifier (with humidistat) in our bathroom which is doing a pretty good job (there’s no convenient place to put an extractor and the bathroom has multiple damp-inducing construction issues which we plan to fix … one day) but perhaps costing more to run than we expected.
This quality of data tells me almost exactly what’s responsible for my power bill. It’s not perfect – I suspect the webcam misses the occasional blink from the meter and there may also be spurious false-positive readings recorded which manifest themselves as horrifying 20-30kW spikes! Effectively polling the meter blink also quantises the data in a way that produces equally-ish spaced horizontal stripes of points around a reading. I’ve got a tiny microcontroller and photocell on order which I’m hoping will allow me to stick (temporarily) something on the face of the meter which will much more precisely capture (by interrupt rather than poll) the timing of the blink’s off-to-on transition.
What I’ve collected with the webcam is around about the quality and quantity of data I expected from my smart meter, so I’m bitterly disappointed by what was installed. I have to say that what’s installed is exactly what the standard says should be installed, so my disappointment is reserved wholly for the government. We shouldn’t have to try this hard to get data about us from devices which are recording us in our homes.