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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.

  • FauxLiving@lemmy.world
    31·
    1 day ago

    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.

    • BluescreenOfDeath@lemmy.worldEnglish
      2·
      22 hours ago

      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.

      • FauxLiving@lemmy.world
        1·
        21 hours ago

        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.