Electrical load planning and demand response programs are often based on the analysis of individual load level measurements obtained from houses or buildings. The identification of individual appliances’ power consumption is essential, since it allows improvements, which can reduce appliances’ power consumption. In this paper, the problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a non-intrusive load monitoring (NILM) technique. A NILM algorithm based on a convolutional neural network is proposed. The proposed algorithm allows simultaneous detection and classification of events without having to perform double processing. As a result, the calculation times can be reduced. Another important advantage is that only the acquisition of current is required. The proposed measurement system is also described in this paper. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house. The most important experimental results are also included and discussed in the paper.

Electric motors and the systems they drive are the single largest electrical end-use; it is estimated that they consume between 43% and 46% of all global electricity consumption. Recent studies show that efficiency values of induction motors can be successfully increased, with improvements up to 20%-30%. In 2009, the European Commission published a new regulation (640/2009) concerning requirements for the eco-compatible design of electric motors. In such a scenario, the experimental determination of induction motor efficiency is getting more and more important, because of the need of placing these motors in the right energy efficiency levels defined by international regulations. The correct motor classification strictly depends on the uncertainty associated with the efficiency determination. According to the IEC 60034-2-1 standard, the efficiency of three-phase induction motors can be determined by applying the direct or indirect efficiency technique. The direct procedure is suggested for low uncertainty efficiency measurements of single-phase induction motors and three-phase motors with rated power ≤1 kW. In this paper, we propose a comparative analysis between direct and indirect efficiency determination for three-phase induction motors, according to the IEC 60034-2-1 standard. The contribution of this paper consists in the comparison between the uncertainty calculations in indirect and direct efficiency determination for induction motors, to investigate the advisability of adopting the direct efficiency determination as reference method, because it is easier to implement and it has a good accuracy. This task is carried out starting from the accuracy requirements for measurement equipment, as demanded by the Guide to the Expression of Uncertainty in Measurement GUM. Some experimental results obtained with the indirect and direct efficiency determinations on a 3-kW three-phase two-pole induction motor are presented and discussed. A final suggestion related to a future version of IEC 60034-2-1 is also reported.

Nonintrusive load monitoring (NILM) systems are used to identify the energy consumption patterns of individual devices in an electrical system, but broadening their market availability is a significant challenge. In this paper, a NILM system using edge processing is proposed, in which energy consumption data are processed directly on the device installed at the monitored facility. Specifically, it uses a sequence-to-point approach based on a convolutional neural network implemented on an Arm Cortex-M7 microcontroller. This paper also reports the results of an extensive 12-month testing phase. The NILM system was installed in two real houses in central Italy to evaluate its installation and potential application in real-world scenarios. This study presents a promising solution that enables the widespread adoption of NILM systems by reducing their implementation cost and complexity and addresses the privacy concerns associated with cloud-based data processing. The results of our real-world testing provide compelling evidence of the potential of the proposed NILM system in various applications, including smart homes, building automation, and industrial energy management.

This paper reports the results of the analyses on the accuracy of algorithms commonly adopted in instruments devoted to the detection and characterization of voltage dips (also called sags). This analysis is particularly interesting because the results of dip measurements are used for calculation of severity levels and site indexes that are important parameters in the assessment of quality level of power supply but also for selecting equipment with proper intrinsic immunity. Anyway, instruments for dip measurement still have unresolved technical and theoretical issues related to the characterization of their metrological performance, so it can be found that different instruments are significantly in disagreement in some actual measurements. This paper moves a step into the direction of deepening the knowledge about the measurement of voltage dips, pointing out the limits because of the adoption of the detection algorithms adopted in agreement with standard. It starts with a discussion about parameters that characterize voltage dips. Then, the analytical calculations of systematic deviations in the event characterization, introduced by the most diffused dip detection algorithms, in simplified measurement situations, are presented, underlining their remarkable impact with particular attention to short-dip event. The obtained relations are experimentally verified on a commercial power quality instrument. Brief remarks about the analysis of the main algorithms, alternative to that of the standard, for voltage dip detection are also reported.

Recently, it has been proposed that obstructive sleep apnea syndrome (OSAS) is characterized by an imbalance in autonomic nervous tone. Pupil size has been considered a valid test for studying the autonomic nervous system (ANS). Pupillometry is a simple and non-invasive tool to assess the size and dynamics of the pupil. The purpose of this study was to evaluate, by pupillometry, the hypothesis that subjects with OSAS present ANS dysregulation. The study group included 10 males aged between 40 and 50 years with polysomnographic diagnoses of mild OSAS. The control group included 10 males with similar ages with an apnea-hypopnea index (AHI) of less than 5, after polysomnography. Pupillometry was performed by digital infrared pupillometer (25 frame/s). Recordings were processed to measure the area of the pupil frame by frame. The subjects underwent four subsequent recordings: infrared light at rest mandible position (RP); infrared light at forced habitual occlusion (FHO); yellow-green light at RP; and yellow-green light at FHO. According to literature, linear and non-linear information was extracted from the recordings. As expected, the two groups did not differ statistically in age and body mass index (BMI), while there was a significant difference in the AHI. In the within-group comparison of pupil size, there were significant differences between RP and FHO under infrared conditions in the control group. There was a significant difference in the determinism percentage (Det%) in the RP infrared condition between the control and OSAS groups. The results of the current study confirm ANS dysregulation in OSAS patients and provide a new possible strategy for studying this pathology by using pupillometry through linear and non-linear mathematical models.

Load monitoring systems make it possible to obtain information on the status of the various loads powered by an electrical system. The term “electrical load” indicates any device or circuit that absorbs energy from the system to which it is connected, and which therefore influences electrical quantities such as power, voltage, and current. These monitoring systems, designed for applications related to energy efficiency, can also be used in other applications. This article analyzes in detail how the information derived from Non-Intrusive Load Monitoring (NILM) systems can be used in order to create Energy Management Systems (EMS), Demand Response (DR), anomaly detection, maintenance, and Ambient Assisted Living (AAL).