Reliable assessment of uncertainty for appliance recognition in NILM using conformal prediction

Abstract

AbstractA primary task of Non‐intrusive Load Monitoring (NILM) is the identification of appliances that are switched on or off. However, state‐of‐the‐art machine learning methods such as deep learning do not express uncertainty of their predictions. Especially in cases where appliances are confused, it is desirable that an NILM system can suggest multiple possible predictions to the end‐user, including its confidence and credibility of any given prediction. This can be achieved using conformal prediction, being an effective way to quantify uncertainty of a given machine learning model. In this work, conformal prediction is introduced for NILM and applied to a neural network. The approach is explained and supported by several examples.