## Future Work  
A promising extension of the proposed system involves its integration with a microcontroller like the ESP32, which provides built-in Wi-Fi and Bluetooth capabilities. The transition would be straightforward, as the ESP32 is compatible with the Arduino IDE, allowing the core logic to be easily ported. This upgrade would enable real-time data transmission to cloud-based IoT platforms capable of executing adaptive algorithms. These algorithms could adjust the model parameters dynamically, in response to changing environmental conditions such as irradiance and temperature. Such an approach would not only improve the accuracy of PV panel modeling but also enable on-the-fly diagnostics and fault detection, enhancing the system's applicability for long-term monitoring and smart grid integration.  
The programmable resistive load can be used as a cost-effective platform to develop and validate Maximum Power Point Tracking (MPPT) algorithms. Instead of a simple sweep, the microcontroller could execute different MPPT strategies (such as perturb and observe, incremental conductance, or particle swarm optimization) by dynamically changing the load to actively track the panel's maximum power point. This allows for direct, empirical benchmarking of algorithm efficiency under real-world conditions.  
Also, by integrating electronic multiplexers or relay-based switching matrices at the input, the system can be extended to sequentially characterize multiple PV panels in an array. The microcontroller would first select a panel, perform the I-V trace, store the data, and then switch to the next panel. This automated process is invaluable for identifying module mismatch, localized faults, or degradation across an entire PV string in both laboratory and educational settings where cost-effective scalability is essential.  
Conceptualization, P.L.O.M., L.V.G.F., R.M.S. and T.A.A.; methodology, P.L.O.M., L.V.G.F. and R.M.S.; software, P.L.O.M., L.V.G.F., E.T.T. and R.M.S.; validation, P.L.O.M. and E.T.T.; formal analysis, P.L.O.M., E.T.T., T.M.B., S.L.S.J., H.V.S., R.M.S. and T.A.A.; investigation, P.L.O.M. and R.M.S.; resources, R.M.S. and T.A.A.; data curation, P.L.O.M., L.V.G.F., E.T.T., T.M.B. and R.M.S.; writing--original draft preparation, P.L.O.M.; writing--review and editing, P.L.O.M., E.T.T., T.M.B., S.L.S.J., H.V.S., R.M.S. and T.A.A.; visualization, P.L.O.M., T.M.B., S.L.S.J., H.V.S., R.M.S. and T.A.A.; supervision, S.L.S.J., H.V.S., R.M.S. and T.A.A.; project administration, R.M.S. and T.A.A.; funding acquisition, R.M.S. and T.A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001. The authors thank the Brazilian National Council for Scientific and Technological Development (CNPq), process numbers 315298/2020-0, 306448/2021-1, 409631/2021-3 and 312367/2022-8, and Araucaria Foundation, process number 51497 and 19.311.894-1, for their financial support.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors acknowledge the Capes, the CNPq, the PROPPG/UTFPR, the DIRPPG/UTFPR (Campus Ponta Grossa), the PPGGEM/UTFPR (Campus Ponta Grossa), the PPGEE/UTFPR (Campus Ponta Grossa), the PPGEE/UTFPR (Campus Ponta Grossa), the DAMEC/UTFPR (Campus Ponta Grossa), the DAELE/UTFPR (Campus Ponta Grossa), and the DAFIS/UTFPR (Campus Ponta Grossa).  
**Conflicts of Interest:** The authors declare no conflicts of interest.