I won't connect my dishwasher to your stupid cloud | Jeff Geerling
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Software developer and open source proponent Jeff Geerling recently purchased a new Bosch 500 dishwasher, only to find it required an app to access certain features. This is his story.
You still are & just don't know it. The power company can tell when & what devices you're using. You'd need a way to store enough power so your appliances never connect directly to the grid, basically a power firewall.
That has fuck-all to do with the guy's complaint, which was about it failing to provide the simple ability to use the advertised features of the machine from the machine's control panel, not anything to do with privacy.
I had assumed that he didn't want to use an app because of privacy concerns, not that he didn't have a way to install the app. We've allowed this behavior to become normalized already though. It is significantly harder now days to find consumer products like routers that don't require an app to manage & use. It's not like either side of the political aisle has been seriously pushing for regulation regarding privacy, when they absolutely love being able to get all that data directly from these conglomerates without a court order.
Thats a new one.. I don't know enough to doubt it, but curious to how. Actual digital communication over power lines, or just recognizable power usage patterns?
It would have to be an inference based on power draw. Not at all accurate or definitive, I don't think. This seems a bit tinfoil-hatty to me.
I'm betting it relies on an assumption that every dishwasher would draw the same amount of current (within reason) as every other dishwasher. The same with every washing machine, every dryer, every AC, and so on. On top of that, all the current draws would need to be unique. If a dryer pulled the same current as an oven then the surveillance people wouldn't know which you were running.
Sure, you could infer a little based on time of day and such, but who's to say the homeowner isn't just running 10 microwaves?
It is based on a machine learning task called classification.
The reason that a machine can detect a face or a cat in a picture without seeing every cat.
Power meters can same energy usage at high frequency, this gives it access to a lot more data to train on.
There's a lot more raw data present in a couple of pictures of a cat than what a power meter has access to, however.
The meter can only see overall amperage draw, and without something to reference that against, it's hard to know what's using all the power.
Was that the dishwasher cutting on, or a chandelier with 20 incandescent bulbs? A microwave, or a hair dryer? Air compressor? Battery charger? Vacuum cleaner?
There are lots of options for things that use power, and any inferences you could draw off of power usage makes too many assumptions. For instance, power draw is increased by the amount of conductor between the thing drawing power, and the meter. So a hair dryer can draw more amps when used in an outlet farther from the meter vs if it's connected to an outlet right next to it. Plus, things draw more or less power based on the work being done. A drill spinning freely will draw less amps than a drill actively drilling into something.
There's just too many variables. The best you could hope to achieve is have a computer say "this household's power draw at this time could have been this selection of different combinations of power draws" which isn't very useful, especially considering how efficient things have gotten. How is the meter to know the difference between me turning on my outdoor lights (4x120w bulbs) and my computer running at full tilt (my high end GPU and CPU consume almost 500w at full load)?
https://www.sciencedirect.com/science/article/abs/pii/S2352467719300748
TL;DR: Math
Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households [...] Our method is implemented as an algorithm combining NILM and load profile simulation. This algorithm, based on a Markov model, allocates an activity chain to each inhabitant of the household, deduces from the whole-house power measurement and statistical data the appliance usage, generate the power profile accordingly and finally returns the share of energy consumed by each appliance category over time.
TL;DR, it's not nearly as granular as you suggest:
https://ars.els-cdn.com/content/image/1-s2.0-S2352467719300748-fx1_lrg.jpg
They can generally characterize the probability that the load is for certain things, but they can't say that your power consumption is because you're using a vacuum cleaner and 7 LED bulbs. They estimate the percentage of your overall consumption that is used by certain things. It's not the same as feeding a LLM a few cat pictures and getting it to identify a cat.
That paper is specifically about low frequency data (under 1 hz) so it does not include fast transient events. Because of that, the amount of information that you can learn is limited.
High frequency sampling can capture fast transients, startup transients and information about the circuit harmonics. This provides a lot more data points to extrapolate from. Modern smart meters are available with high frequency (several kilohertz) measurements, they may not be deployed in your utility section, but they are used.
There is more to NILM (https://en.m.wikipedia.org/wiki/Nonintrusive_load_monitoring) than that one paper.
From the wiki:
NILM can detect what types of appliances people have and their behavioral patterns. Patterns of energy use may indicate behavior patterns, such as routine times that nobody is at home, or embarrassing or illegal behavior of residents. It could, for example, reveal when the occupants of a house are using the shower, or when individual lights are turned on and off.
If the NILM is running remotely at a utility or by a third party, the homeowner may not know that their behavior is being monitored and recorded.
A stand-alone in-home system, under the control of the user, can provide feedback about energy use, without revealing information to others. Drawing links between their behavior and energy consumption may help reduce energy consumption, improve efficiency, flatten peak loads, save money, or balance appliance use with green energy availability. However the use of a stand-alone system does not protect one from remote monitoring.
The accuracy and capability of this technology is still developing and is not 100% reliable in near-real-time, such that complete information is accumulated and analyzed over periods ranging from minutes to hours.
My utility company told me they could do this, but I know for a fact they cannot. My power meter broadcasts its instantaneous reading in short plain text packets at a frequency once every few seconds. They told me all my power usage was hot water. I'm sure it's HVAC and computers, which didn't even show up in their list.
It depends on the kind of meter that your utility uses and how they have it configured.
Smart meters can measure that data at higher frequencies (up to several kilohertz) and from that data you can detect signatures that identify devices inside your house and even, in some cases, what they're doing. For example, when your washer turns on it runs a pump (which draws a specific load) for a set amount of time and then goes through a cycle of running a motor to agitate the load, which draws energy in a specific way as it turns back and forth. When you turn on an LED light, it runs at a steady rate and draws the same amount of energy. When your AC runs it draws a different amount of current than the washer or LED.
With enough data, over time, you can determine which devices are in the house and when compared to a database of known signatures you can classify the device (All Samsung Refrigerator Model 23e4234 work the same way).
Here's some articles talking about it:
https://www.sciencedirect.com/science/article/abs/pii/S2352467719300748
https://energyinformatics.springeropen.com/articles/10.1186/s42162-019-0096-9
You're gonna have to redo that last sentence there. I'm not catching the drift.
What data are they gathering? Like, what specific info from the appliance can tell the power company what it is you are running?
Smart meters can measure, instantaneously, I and V at high frequencies (up to several kilohertz) and by looking at long term and transient event signatures in that data it is possible to classify the loads as coming from specific kinds of devices (down to individual model numbers for known devices).
Even if you have an older smart meter (not the analog ones) that can only sample at a few hz you can still do the same kinds of things but with less accuracy:
https://energyinformatics.springeropen.com/articles/10.1186/s42162-019-0096-9
Electrical load signatures have been demonstrated to contain a great information content. This bears promising potential for the application of signal processing algorithms to extract relevant high-level (i.e., abstract) features from the possibly large volume of consumption data. One prominent example for load signature analysis is NIALM, first introduced by George Hart in (Hart 1985; 1992). A range of approaches to detect activities and identify the causing appliances have been presented in literature, e.g., in (Zeifman and Roth 2011), and numerous companies have added disaggregation products to their portfolio in recent years. The process of inferring appliance activity through NIALM is composed of three major steps: Data acquisition, feature extraction, and load identification (Zoha et al. 2012). All of which have been extensively investigated in research, resulting in a large set of proposed algorithms, methods, and features, e.g., in (Jin et al. 2011; De Baets et al. 2017; Leeb et al. 1995; Bergés et al. 2011; Kahl et al. 2017). Event detection is commonly a part of the feature extraction step and used to detect changes in appliance operation from the data.
Event detection algorithms can be categorized by their analysis of steady state or transient information. Algorithms relying on steady state information, such as power consumption readings during the periods before and after a state transition, are well-suited to detect events of appliances with a constant power consumption in each of their modes of operation. The second option is to operate on transient signatures, i.e., the power consumption changes that can be observed during an event. They allow for the characterization of a device and its mode of operation by the unique shape of its power consumption during state changes (Zoha et al. 2012; Anderson et al. 2012).
There's stuff like ripple control to tell appliances to lower consumption. Pretty archaic and rare these days. There's nothing I know of that communicates to the utility.
I have no idea what John is talking about or why he brought this concept up.
Both actually... most smart meters are now smart enough to identify specific appliances, some have raised concerns & published papers showing that they may even be able to determine what TV channel an individual is watching. Power companies of course are also implementing machine learning to gather even more detailed information & behavioral patterns, including identifying patterns & usage of each household member:
Actual digital communication over power lines
Is a real thing, though not at all in the way the parent comment seems to be discussing it.
Wouldn't that monitoring need to be really close to the house? Otherwise I'd think the capacitance of the lines would even out any tiny profile surges. So I think it'd need to be in the power meter. I'd think you could verify or rule that out pretty quickly by looking if the hardware is capable of that sort of tracking.
