Right to Repair

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).