journal article·2025-11-22Open access

Real-Time Solar Irradiance Monitoring: An FPGA-Based Approach for Data Visualization

Rubén De Jesús Rivera-Medina, Oscar Osvaldo Ordaz-García, Angel García-Duran, Salvador Ibarra-Delgado, Remberto Sandoval-Aréchiga, José Ricardo Gómez-Rodríguez, Víktor I. Rodríguez-Abdalá, Héctor A. Guerrero-Osuna

Applied Sciences· Volume 15· Issue 23

Abstract

Measuring solar irradiance helps maximize efficiency in photovoltaic power plants. Devices for detecting solar irradiance, such as pyranometers, are difficult to calibrate and extremely expensive. Given these limitations, they are rarely used in photovoltaic power plants. This study explores the feasibility of monitoring solar radiation using an FPGA and low-cost, ambient light and power sensors. The monitoring of irradiance and illuminance was graphed and stored in a software application. Unlike traditional monitoring systems, this approach directly measured the electrical power generated by the cell as a proxy for solar irradiance. To validate the accuracy of the system, the measurements obtained were compared with a professional reference pyranometer (INIFAP). Linear regression analysis showed a linear correlation (R2) of 0.8115 between the system measurements and the high-cost reference pyranometer. In addition, accuracy was quantified using the Root Mean Square Error (RMSE) of 21.68 W/m2. The regression slope showed an average photovoltaic conversion efficiency of 15.44% during the test period, validating the system as an accurate, linear, and reliable tool for monitoring irradiance.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Photovoltaic System Optimization Techniques · Solar Radiation and Photovoltaics · Solar Thermal and Photovoltaic Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas “Francisco García Salinas”, Campus UAZ Siglo XXI, Carretera Zacatecas-Guadalajara km 6, Ejido la Escondida, Zacatecas 98160, Mexico

References (42)

Show source-provided references
1. Indium oxynitride (InNO) radiation sensors calibration10.1109/JSEN.2017.2670080 ↗
2. Ma, J., Bi, Z., Shi, Y., Man, K.L., Pan, X., and Wang, J. (2016, January 25–28). OL-SVR based soft-sensor for real-time estimation of solar irradiance. Proceedings of the IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Jeju, Republic of Korea.10.1109/APCCAS.2016.7803999 ↗
3. Solar energy radiation measurement with a low–power solar energy harvester10.1016/j.compag.2018.06.011 ↗
4. Modelling solar energy input in greenhouses10.1016/S0038-092X(00)00054-2 ↗
5. A neural network-based low-cost solar irradiance sensor
6. Aljaghoub, H., Alashkar, A., Alami, A.H., and Tawalbeh, M. (2022, January 21–24). Prediction of Weather Patterns Using Linear Angstrom Model. Proceedings of the Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates.10.1109/ASET53988.2022.9734942 ↗
7. Performance evaluation of silicon-based irradiance sensors versus thermopile pyranometer10.1109/JPHOTOV.2020.3038342 ↗
8. Toolflows for mapping convolutional neural networks on FPGAs: A survey and future directions10.1145/3186332 ↗
9. Remote-Control System of High Efficiency and Intelligent Street Lighting Using a ZigBee Network of Devices and Sensors10.1109/TPWRD.2012.2212215 ↗
10. Kuo, Y.-S., Pannuto, P., Schmid, T., and Dutta, P. (2012, January 6). Reconfiguring the software radio to improve power, price, and portability. Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, Toronto, ON, Canada.10.1145/2426656.2426683 ↗
11. Marmote SDR: Experimental platform for low-power wireless protocol stack research10.3390/jsan2030631 ↗
12. Dello Sterpaio, L., Marino, A., Nannipieri, P., Dinelli, G., Davalle, D., and Fanucci, L. (2019). A Complete EGSE Solution for the SpaceWire and SpaceFibre Protocol Based on the PXI Industry Standard. Sensors, 19.10.3390/s19225013 ↗
13. Introduction to Field Programmable Gate Arrays (FPGA)10.1007/978-981-19-0615-2_1 ↗
14. FPGA-Based High-Performance Computing Platform for Cryptanalysis of AES Algorithm10.1007/978-981-32-9515-5_60 ↗
15. Hernández-Calvino, M., Geninatti, S.R., and Benavides-Benítez, J.I. (2008, January 3–5). Developing an MMX extension for the MicroBlaze soft processor. Proceedings of the 2008 International Conference on Reconfigurable Computing and FPGAs, Cancun, Mexico.10.1109/ReConFig.2008.9 ↗
16. Li, Y., Zhan, J., Jiang, W., Wu, J., and Zhu, J. (2020, January 13–16). An FPGA based Network Interface Card with Query Filter for Storage Nodes of Big Data Systems. Proceedings of the 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), Beijing, China.10.1109/ASP-DAC47756.2020.9045372 ↗
17. Chen, H., and Zeng, Z. (2020, January 12–14). Design and Implementation of TSIP Interface Driver Based on FPGA. Proceedings of the 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.10.1109/ITNEC48623.2020.9085040 ↗
18. Improved precision of the outdoor performance measurements of photovoltaic modules by using the photovoltaic irradiance sensor10.1016/j.solener.2020.09.002 ↗
19. Achleitner, S., Kamthe, A., Liu, T., and Cerpa, A.E. (2014, January 15–17). SIPS: Solar irradiance prediction system. Proceedings of the IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, Berlin, Germany.10.1109/IPSN.2014.6846755 ↗
20. Estimation of irradiance and temperature using photovoltaic modules10.1016/j.solener.2014.08.040 ↗
21. Novel field-support vector regression-based soft sensor for accurate estimation of solar irradiance10.1109/TCSI.2017.2746091 ↗
22. A comparative study of evolutionary computation techniques for solar cells parameter estimation
23. An Adaptive resistance perturbation based MPPT algorithm for Photovoltaic applications10.1109/ACCESS.2020.3034283 ↗
24. Grohe, A., Knorz, A., Alemán, M., Harmel, C., Glunz, S.W., Preu, R., and Willeke, G.P. (2019). Fraunhofer-Institute-for-Solar-Energy-Systems. Photovoltaics Report, Fraunhofer ISE.
25. Driesse, A., Zaaiman, W., Riley, D., Taylor, N., and Stein, J.S. (2015, January 14–18). Indoor and outdoor evaluation of global irradiance sensors. Proceedings of the 31st European Photovoltaic Solar Energy Conference, Hamburg, Germany.
26. Vignola, F., Peterson, J., Chiu, C.-Y., Dooraghi, M., Sengupta, M., and Mavromatakis, F. (2024, January 8–11). Comparison of pyranometers and reference cells on fixed and one-axis tracking surfaces. Proceedings of the ASES National Solar Conference Proceedings, Boulder, CO, USA.
27. Worldwide evaluation and correction of irradiance measurements from personal weather stations under all-sky conditions10.1016/j.solener.2020.06.073 ↗
28. Fault-diagnosis architecture for large-scale photovoltaic power plants that does not require additional sensors10.1109/JPHOTOV.2019.2903870 ↗
29. Photovoltaic-model-based solar irradiance estimators: Performance comparison and application to maximum power forecasting10.1109/TSTE.2017.2714690 ↗
30. An improved model-based maximum power point tracker for photovoltaic panels10.1109/TIM.2013.2277579 ↗
31. Tan, R.H., Tai, P.L., and Mok, V. (2013, January 25–27). Solar irradiance estimation based on photovoltaic module short circuit current measurement. Proceedings of the 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), Kuala Lumpur, Malaysia.10.1109/ICSIMA.2013.6717943 ↗
32. Liu, Y., Bebic, J., Kroposki, B., De Bedout, J., and Ren, W. (2008, January 17–18). Distribution system voltage performance analysis for high-penetration PV. Proceedings of the 2008 IEEE Energy 2030 Conference, Atlanta, GA, USA.10.1109/ENERGY.2008.4781069 ↗
33. A composable method for real-time control of active distribution networks with explicit power setpoints. Part I: Framework10.1016/j.epsr.2015.03.023 ↗
34. Proof of concept of an irradiance estimation system for reconfigurable photovoltaic arrays10.3390/en8076641 ↗
35. Two FPGA-oriented high-speed irradiance virtual sensors for photovoltaic plants10.1109/TII.2015.2462293 ↗
36. Ordaz-García, O.O., Ortiz-López, M., Quiles-Latorre, F.J., Arceo-Olague, J.G., Solís-Robles, R., and Bellido-Outeiriño, F.J. (2020). DALI Bridge FPGA-Based Implementation in a Wireless Sensor Node for IoT Street Lighting Applications. Electronics, 9.10.3390/electronics9111803 ↗
37. Ams-OSRAM-Group (2023, January 04). Product Document-TSL2591—Light-to-Digital Converter. 2018; pp. 1–34. Available online: https://ams.com/documents/20143/36005/TSL2591_DS000338_6-00.pdf/090eb50d-bb18-5b45-4938-9b3672f86b80.
38. Texas-Instruments (2023, January 04). INA260 Precision Digital Current and Power Monitor with Low-Drift, Precision Integrated Shunt. 2016; pp. 1–37. Available online: https://www.ti.com/lit/ds/symlink/ina260.pdf?HQS=dis-mous-null-mousermode-dsf-pf-null-wwe&ts=1618596812471&ref_url=https%253A%252F%252Fwww.mouser.mx%252F.
39. Solar Cells as a Pedagogical Alternative in the Teaching of Electricity
40. Chu, P.P. (2011). FPGA Prototyping by VHDL Examples: Xilinx Spartan-3 Version, John Wiley & Sons.
41. Zhejiang-Dongshuo-New-Energy-Co. Ltd (2023, January 23). Cómo Calcular el Ángulo Correcto Para Paneles Solares en Sistemas de Energía Solar. Conocimiento—DS New Energy. 2019; p. 1. Available online: https://www.dsisolar.com/info/how-to-figure-the-correct-angle-for-solar-pane-37641121.html.
42. (2025, November 10). Google-Earth. Available online: https://earth.google.com/earth/d/1zEbwNyUT1hlqSBQZI74hIh3ZctqhwdjP?usp=sharing.