The measurement of the energy consumption of electrical appliances, where the meter is installed at a single point on the main input circuit of the building, is called non-intrusive load monitoring (NILM). The NILM method can distinguish the loads that are currently active and break down how the loads consume electricity. A microcontroller with embedded software was selected to read the data into the NILM method process at a low sampling rate every 1 s or 1 Hz. The measured data and the data obtained by the NILM algorithm were displayed via an internet platform. This article presents an alternative low-cost embedded NILM system for household energy conservation with a low sampling rate, which could identify electrical appliances such as an air conditioner, refrigerator, television, electric kettle, electric iron, microwave oven, rice cooker, and washing machine. Four features of symmetry pattern were extracted, containing information on the value of active power change, the value of reactive power change, the number of intersection points between the active power data and the reference line, and an estimation of an equation for the starting characteristics of the electrical equipment. The proposed NILM system was tested in a selected test house that used a single-phase power system. A typical meter was also installed to compare the results with the proposed NILM. The validity of the tests was checked for 1 month in 3 houses to analyze the results. The proposed method was able to detect 91.3% of total events. The accuracy of the average ability of the system to disaggregate devices was 0.897. The accuracy value for total power consumption was 0.927. The continuous data recording of the NILM method provides information on the behavior of electrical appliances that can be used for maintenance and warnings.

This paper presented an optimization and material enhancement framework for improving the efficiency of a 1 HP Permanent Split Capacitor (PSC) motor toward IE2/IE3 efficiency classes. The proposed approach integrated Design of Experiments (DOE) using the Taguchi method with loss-based analysis to investigate the influence of key design parameters, including stator stack height, capacitor value, and silicon steel grade on PSC motor efficiency. Taguchi L8 and L9 orthogonal arrays were applied to evaluate parameter interactions and identify dominant factors affecting motor performance. To enhance predictive capability, a Response Surface Methodology (RSM) model was developed based on experimental data to establish the relationship between design variables and motor efficiency within the investigated operating range. The resulting efficiency map was used to identify high-efficiency operating regions and support practical PSC motor design evaluation. Experimental validation under multi-load operating conditions confirmed that the optimized motor achieved an efficiency improvement from 76.1% to 80.4% (4.6% absolute increase), with less than 2% deviation between simulation and experimental results. The optimized motor also demonstrated improved operating behavior across varying speed and load conditions while maintaining practical operating stability. The proposed framework provided a practical and simplified approach for PSC motor efficiency improvement under the investigated operating conditions and offers an alternative to computationally intensive optimization approaches for industrial motor applications.

This paper presents a physics-guided optimization and material enhancement framework for improving the efficiency of a 1 HP Permanent Split Capacitor (PSC) motor toward IE2/IE3 standards. The proposed approach integrates Design of Experiments (DOE) using the Taguchi method with loss modeling to enable both parameter-level and material-level optimization. Key design variables, including stator stack height, capacitor value, and silicon steel grade, are systematically analyzed using Taguchi L18 and L9 orthogonal arrays and explicitly linked to electromagnetic loss components, including copper, core, and mechanical losses, enabling physically interpretable optimization. To enhance predictive capability, a Response Surface Methodology (RSM) model is developed based on experimental data to establish a continuous relationship between design variables and motor efficiency. The results are further represented as an efficiency map, which identifies high-efficiency operating regions and supports scalable design exploration toward IE4 performance. Experimental validation under multi-load conditions confirms that the optimized motor achieves an efficiency improvement from 76.1% to 80.4% (4.6% absolute increase), with less than 2% deviation from simulation results. The proposed method offers a low-complexity and cost-effective alternative to conventional FEA-based approaches and is suitable for practical industrial applications. Furthermore, the framework is scalable and provides a pathway toward IE4-level efficiency through integration of advanced materials, thermal considerations, and multi-objective optimization.