Load Profile Analysis in Electrical Systems: The Impact of Electrical Signature and Monitoring Quality in the Energy Digitalization Process

In the context of energy digitization, Load Profiling can be a useful tool for making decisions about the use and the health of an electrical load and could be adopted for strategic services related to the energy efficiency, characterization, prediction, optimization, and diagnosis of monitored systems. To accomplish this task, it is important a spread of a new generation of smart meters. In this scenario, useful information for the developers of services based on Load Profiling could be the minimum metrological characteristics of smart meters, that is, the monitoring quality (MQ), and the electrical parameters to extract from the electrical signature to have a reliable Load Profiling process, that is, the electrical signature quality (ESQ). This article tries to answer these questions by investigating the impact of the MQ and the ESQ on the performance of Load Profiling. The main results of the article are: 1) generally the Load Profiling process requires metrological characteristics lower than those required for energy billing reducing submetering infrastructure costs; 2) the increasing number of energy features adopted in the Load Profiling not always improves reliability and accuracy; this happens when many of the features considered do not have great sensitivity with respect to changes in the energy states for the considered case study; 3) the use of functions that measure the sensitivity of a feature to the Load Profiling process, such as the considered kernel density estimation (KDE), and a suitable threshold process can delete the parameters with poor sensitivity and can greatly improve the Load Profiling reliability; and 4) the method considered in the article could help to analyze Load Profiling problems related to other physical quantities (i.e., thermal energy profiling or even multiphysical systems), allowing to the definition of a target MQ and selecting a minimum number of useful features to be adopted.