Proficiency of Power Values for Load Disaggregation

TL;DR

This paper introduces an information-theoretic framework to evaluate the effectiveness of load disaggregation techniques, focusing on the concept of proficiency derived from entropy and mutual information, supported by both artificial and real-world data.

Contribution

It presents a novel information coding perspective and defines proficiency to assess the distinguishability of device configurations in load disaggregation.

Findings

Proficiency depends on device running probability and power states.

Mutual information and entropy quantify disaggregation difficulty.

Application demonstrated with artificial and real datasets.

Abstract

Load disaggregation techniques infer the operation of different power consuming devices from a single measurement point that records the total power draw 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 to support its future improvement. In typical cases of quantity and type of devices and their respective power consumption, not all possible device configurations can be mapped to distinguishable power values. We introduce the term of proficiency to describe the suitability of a device set for load disaggregation. We provide the notion and calculation of entropy of initial device states, mutual information of power values and the resulting uncertainty coefficient or proficiency. We show that the proficiency is highly dependent from the device running probability especially for devices with multiple states of power consumption. The application of the concept is demonstrated by exemplary artificial data as well as with actual power consumption data from real-world power draw datasets.