Accurate photovoltaic (PV) panel characterization is critical for optimizing renewable energy systems, but it is often hindered by the high cost of commercial tracers or the slow, error-prone nature of manual methods. This paper presents a low-cost, Arduino-based I–V curve tracer that overcomes these limitations through fully automated resistive load switching. By integrating a relay-controlled resistor bank managed by a single microcontroller, the system eliminates the need for manual intervention, enabling rapid and repeatable measurements in just 45 s. This rapid acquisition is a key advantage over manual systems, as it minimizes the impact of fluctuating environmental conditions and ensures the resulting I–V curve represents a stable operating point. Compared to commercial alternatives, our open-source solution offers significant benefits in cost, portability, and flexibility, making it ideal for field deployment. The system’s use of fixed, stable resistive loads for each measurement point also ensures high repeatability and straightforward comparison with theoretical models. Experimental validation demonstrated high agreement with a single-diode PV model, achieving a mean absolute percentage error (MAPE) of 4.40% against the manufacturer’s data. Furthermore, re-optimizing the model with field-acquired data reduces the MAPE from 18.23% to 7.06% under variable irradiance. This work provides an accessible, robust, and efficient tool for PV characterization, democratizing access for research, education, and field diagnostics.

The efficient recovery of waste heat is essential for improving sustainability in industrial and urban energy systems. This study presents the experimental evaluation of a passive heat recovery unit composed of finned thermosyphons and Bismuth Telluride (Bi₂Te₃) thermoelectric generators (TEGs). The primary objective was to characterize its simultaneous thermal recovery and electrical generation capabilities under airflow and temperature conditions simulating low-grade industrial exhaust streams. The system was tested in an open-loop wind tunnel simulating exhaust gases under air velocities of 0.6, 1.1, and 1.7 m/s. Heat was transferred to the TEGs through finned thermosyphons, enabling power generation via the Seebeck effect. The passive heat exchange mechanism successfully recovered up to 250.9 W of thermal power, preheating the inlet air by a maximum of 9.5 °C with a peak thermal effectiveness of 44.4%. Simultaneously, the system achieved a maximum temperature difference of 30.0 °C across the thermoelectric modules, generating a total electrical power of 163.7 mW (81.8 mW per TEG). This dual-purpose operation resulted in a maximum overall first-law efficiency of 9.38% and an electrical power density of 52.20 W/m² from the low-grade thermal stream. These results confirm the technical feasibility of this compact, passive, and maintenance-free design, highlighting its potential for integration into applications like district heating or industrial ventilation, where balancing thermal and electrical outputs is crucial.

Accurate photovoltaic (PV) panel characterization under real environmental conditions is critical for optimizing renewable energy systems. However, commercial I-V curve tracers are often expensive, inflexible, and unsuitable for rapid field deployment. This paper presents a low-cost, Arduino-based data acquisition (DAQ) system capable of real-time I-V curve tracing using automated resistive load switching in a single microcontroller. The system integrates current and voltage measurements with a relay-controlled resistor bank, enabling sequential load variation without manual intervention. A total of 23 unique resistor combinations were programmed, providing fast and granular coverage of the PV panel’s operating range. Experimental validation was conducted on a commercial PV panel under natural sunlight conditions. The system total data acquisition time was 45 seconds and achieved high agreement with a single-diode PV model, with a mean absolute percentage error (MAPE) of 4.40% under standard conditions (manufacturer’s data). When re-optimized with field-acquired data, the model’s accuracy improved significantly, reducing the MAPE from 18.23% to 7.06% under variable irradiance. This open-source solution provides a cost-effective, portable, and scalable alternative to commercial tracers, with potential for integration into IoT-enabled PV monitoring platforms. Its design supports research, education, and deployment in low-resource environments where accessible renewable energy diagnostics are essential.