Proficiency of Power Values for Load Disaggregation

Load disaggregation techniques infer the operation of different power-consuming devices from a single measurement point that records the total power drawn over time. Thus, a device consuming power at the moment can be understood as information encoded in the power draw. However, similar power draws or similar combinations of power draws limit the ability to detect the currently active device set. We present an information coding perspective of load disaggregation to enable a better understanding of this process and support its future improvement. In typical cases of quantity and types of devices and their respective power consumption, not all possible device configurations can be mapped to distinguishable power values. We introduce the term proficiency to describe the suitability of a device set for load disaggregation. We provide the notion and calculation of entropy of the initial device states, mutual information of power values, and the resulting uncertainty coefficient or proficiency. We show that the proficiency is highly dependent on the device running probability, especially for devices with multiple states of power consumption. The application of the concept is demonstrated by artificial data as well as with actual power consumption data from real-world power draw data sets.