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Load disaggregation techniques infer the operation of different powerconsuming devices from a single measurement point that records the total powerdraw over time. Thus, a device consuming power at the moment can be understoodas information encoded in the power draw. However, similar power draws orsimilar combinations of power draws limit the ability to detect the currentlyactive device set. We present an information coding perspective of loaddisaggregation to enable a better understanding of this process and to supportits future improvement. In typical cases of quantity and type of devices andtheir respective power consumption, not all possible device configurations canbe mapped to distinguishable power values. We introduce the term of proficiencyto describe the suitability of a device set for load disaggregation. We providethe notion and calculation of entropy of initial device states, mutualinformation of power values and the resulting uncertainty coefficient orproficiency. We show that the proficiency is highly dependent from the devicerunning probability especially for devices with multiple states of powerconsumption. The application of the concept is demonstrated by exemplaryartificial data as well as with actual power consumption data from real-worldpower draw datasets.

Proficiency of Power Values for Load Disaggregation