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.
Highlights
Nowadays, economic development has led to a steady increase in the demand for electricity and related advanced services
The performance of the non-intrusive load monitoring (NILM) system was assessed by conducting acquisitions, during which various loads were turned ON and OFF for a total of over 519 events
These parameters were obtained using the number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN)
Summary
Economic development has led to a steady increase in the demand for electricity and related advanced services. Through non-intrusive monitoring, i.e. measuring the total power absorbed by the system and deducing the contributions of the individual loads from this, through the use of specific algorithms. In this second case, an extremely simple and compact measurement system is obtained, at the expense of greater complexity from the point of view of processing [1]. Hart in 1985 [11] This algorithm was based on a detection of the edges in the aggregate power profile, followed by a clustering operation and subsequent matching based on the value of the absorbed power and on the on and off time.
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