Measurements for non-intrusive load monitoring through machine learning approaches

Abstract

The topic of non-intrusive load monitoring (NILM) has seen a significant increase in research interest over the past decade, which has led to a significant increase in the performance of these systems. Nowadays, NILM systems are used in numerous applications, in particular by energy companies that provide users with an advanced management service of different consumption. These systems are mainly based on artificial intelligence algorithms that allow the disaggregation of energy by processing the absorbed power signal over more or less long time intervals (generally from fractions of an hour up to 24 h). Less attention was paid to the search for solutions that allow non-intrusive monitoring of the load in (almost) real time, that is, systems that make it possible to determine the variations in loads in extremely short times (seconds or fractions of a second). This paper proposes possible approaches for non-intrusive load monitoring systems operating in real time, analysing them from the point of view of measurement. The measurement and post-processing techniques used are illustrated and the results discussed. In addition, the work discusses the use of the results obtained to train machine learning algorithms that allow you to convert the measurement results into useful information for the user.