Intrusive vs non-intrusive monitoring
- Intrusive metering refers to the deployment of one monitor per appliance. This is clearly intrusive since it requires access to each appliance to install such equipment. This has the benefit in that the only uncertainty in such monitoring is due to inaccuracies in the metering hardware.
- Non-intrusive metering refers to the deployment of one (or sometimes two) meters per household. This is clearly less intrusive, since it does not provide any inconvenience besides the installation of government mandated smart meters. However, this has the disadvantage that the disaggregation process is likely to introduce further inaccuracies.
Supervised vs unsupervised training
- Supervised training (a.k.a manual setup) refers to performing disaggregation with the aid of labelled appliance data, generally from the same home in which disaggregation is performed. The training data normally consists of sub-metered appliance power data, or a phase in which appliances are turned on one by one, and labelled manually.
- Unsupervised training (a.k.a automatic setup) refers to performing disaggregation without any training data from the household in which disaggregation is being performed. However, without any notion of what appliances exist or how they behave, at best a system can only identify distinct appliances (e.g. appliance 1, appliance 2), and cannot label them with an appliance name (e.g. refrigerator or washing machine).
Event-based vs non event-based disaggregation
- Event-based disaggregation refers to methods which have distinct event detection (e.g. something switched on at 12pm) and event classification (e.g. it was the washing machine). These approaches are often identifiable by a sequential pipeline of algorithms (data collection -> smoothing -> edge detection -> classification). A core advantage of event-based approaches is that decisions are made sequentially, and therefore can easily be deployed as a real-time system.
- Non event-based disaggregation refers to methods which combine event detection and classification into a single process, in which both are inferred simultaneously. These are often identifiable by their use of time series models, which are able to reason over a sequence of data. The advantage of non event-based approaches is that high confidence decisions can affect those that are likely to surround it (e.g. a refrigerator is likely to turn on 30 minutes after its last cycle ended).
High frequency vs low frequency sampling
- High frequency sampling generally refers to meters which sample the current and voltage of a wire at a rate in the order of thousands of times per second (kHz). At this rate, information such as reactive power and current harmonics can be calculated, which are useful features for classification. However, few smart meters are likely to report data at this granularity.
- Low frequency sampling generally refers to meters which sample at between once per second and once per hour. When reported at this rate, active power is indistinguishable from reactive power, and no harmonic content is available. This is the reporting rate of most smart meters.
Steady-state vs transient-state analysis
- Steady-state analysis divides a power series into periods of constant power during which no appliances change state. The differences between these levels of constant power are then used to infer which state change(s) had taken place.
- Transient-state analysis uses the patterns between steady states to classify appliance state changes. However, it is necessary to sample at a high frequency in order to extract transient features for most appliances.