Design and Evaluation of a Low-Cost Automated Sunshine Duration Recorder  with Solar Cell Based on Internet of Things

Mohammad Attar  Gibran; Adrian Renaldi  Rachmat; Afif Izaaz; Neil Farel Rindra  Tempo; Gabrielle Luoise  Abraham; Vasco Yehezkiel  Sidauruk; Kris Anderson P.  Saragih

doi:10.5614/joki.2026.18.1.13

Authors

Mohammad Attar Gibran Badan Meteorologi Klimatologi dan Geofisika, Jakarta Pusat 10610, Indonesia
Adrian Renaldi Rachmat Indonesian Agency for Meteorology, Climatology, and Geophysics, Central Jakarta 10610, Indonesi
Afif Izaaz Indonesian Agency for Meteorology, Climatology, and Geophysics, Central Jakarta 10610, Indonesi
Neil Farel Rindra Tempo Indonesian Agency for Meteorology, Climatology, and Geophysics, Central Jakarta 10610, Indonesi
Gabrielle Luoise Abraham Indonesian Agency for Meteorology, Climatology, and Geophysics, Central Jakarta 10610, Indonesi
Vasco Yehezkiel Sidauruk Indonesian Agency for Meteorology, Climatology, and Geophysics, Central Jakarta 10610, Indonesi
Kris Anderson P. Saragih Indonesian Agency for Meteorology, Climatology, and Geophysics, Central Jakarta 10610, Indonesi

Keywords:

Sunshine duration, solar cell, Campbell-Stokes, Telegram bot

Abstract

Sunshine duration (SD) is an important meteorological parameter for climate analysis and solar energy planning. In Indonesia, SD measurement still heavily relies on manual Campbell–Stokes (CS) recorders, which are prone to subjective errors, labor-intensive, and unreliable under cloudy conditions. This study develops a low-cost IoT-based SD logger using a solar cell and an INA219 sensor to estimate direct solar radiation. An ESP32 microcontroller processes the data, supported by an RTC module and an SD card for timestamping and local storage. Calibration was performed against Copernicus Atmosphere Monitoring Service (CAMS) satellite data, applying the WMO pyrheliometric threshold of 120 W/m². Remote access is provided via a Telegram bot. A 14-day field test yielded RMSE = 104.8 W/m², MAE = 56.1 W/m², and stronger correlation against CAMS (R² = 0.640, r = 0.802) than CS (r = 0.749). The mean daily SD difference was 1.09 hours against CAMS and 2.03 hours against CS. Binary classification for sunshine detection achieved an accuracy of 88.4%, precision of 79.3%, recall of 77.2%, and F1-score of 78.2%. This prototype offers an automated, accurate, weatherproof, and remotely accessible alternative to conventional CS recorders, with strong potential to advance SD monitoring and modernize meteorological infrastructure in Indonesia.

References

WMO, Guide to Instruments and Methods of Observation, vol. V, no. 8. 2023. https://wmo.int/guide-instruments-and-methods-of-observation-wmo-no-8-0.

Á. B. da Rocha, E. de M. Fernandes, C. A. C. Dos Santos, J. M. T. Diniz, and W. F. A. Junior, “Development and validation of an autonomous system for measurement of sunshine duration,” Sensors (Switzerland), vol. 20, no. 16, pp. 1–12, 2020. https://doi.org/10.3390/s20164606.

J. Fan et al., “Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China,” Renew. Sustain. Energy Rev., vol. 100, pp. 186–212, 2018. https://doi.org/10.1016/j.rser.2018.10.018.

J. Almorox and C. Hontoria, “Global solar radiation estimation using sunshine duration in Spain,” Energy Convers. Manag., vol. 45, no. 9–10, pp. 1529–1535, 2004, https://doi.org/10.1016/j.enconman. 2003.08.022.

Á. B. Rocha, E. D. M. Fernandes, C. A. C. Santos, and J. M. T. Diniz, “Development of a Real-Time Surface Solar Radiation Measurement System Based on the Internet of Things ( IoT ),” Sensors (Switzerland), 2021, https://doi.org/10.3390/ s21113836.

S. Pashiardis, A. Pelengaris, and S. A. Kalogirou, “Comparison of Sunshine Duration Measurements between the Campbell–Stokes Sunshine Recorder and Three Automatic Sensors at Three Locations in Cyprus,” Appl. Sci., vol. 13, no. 22, 2023. https://doi.org/10.3390/app132212393.

L. Hannak, K. Friedrich, F. Imbery, and F. Kaspar, “Comparison of manual and automatic daily sunshine duration measurements at German climate reference stations,” Adv. Sci. Res., vol. 16, no. 1994, pp. 175–183, 2019. https://doi.org/10.5194/asr-16-175-2019.

IESR, “Beyond 207 Gigawatts : Unleashing Indonesia’s Solar Potential,” 2021. [Online]. Available: https://iesr.or.id/2021/03/Unleashing-Indonesias-Solar-Potential

Kementrian Energi dan Sumber Daya Mineral, “Pengembangan Energi Surya Di Indonesia ‘Peta Jalan Pengembangan PLTS Atap : Menuju Bali Mandiri Energi,’” Denpasar, 2019. [Online]. Available: https://www.greenpeace.org/planet4-indonesia-stateless/

C. Tang, Y. Zhu, Y. Wei, F. Zhao, X. Wu, and X. Tian, “Spatiotemporal Characteristics and Influencing Factors of Sunshine Duration in China from 1970 to 2019,” Atmosphere (Basel)., vol. 13, no. 12, 2022. https://doi.org/10.3390/atmos13122015.

D. Matuszko, “A comparison of sunshine duration records from the Campbell-Stokes sunshine recorder and CSD3 sunshine duration sensor,” Theor. Appl. Climatol., vol. 119, no. 3–4, pp. 401–406, 2015. https://doi.org/10.1007/s00704-014-1125-z.

M. Owczarek and M. Malinowska, “Manual and Automatic Measurements of Sunshine Duration in Cassubian Lakeland (Northern Poland),” Atmosphere (Basel)., vol. 14, no. 2, 2023. https://doi.org/10.3390/atmos14020244.

D. J. Baumgartner et al., “A comparison of long-term parallel measurements of sunshine duration obtained with a Campbell-Stokes sunshine recorder and two automated sunshine sensors,” Theor. Appl. Climatol., vol. 133, no. 1–2, pp. 263–275, 2018. https://doi.org/10.1007/s00704-017-2159-9.

F. Sabatini, “Setting up and managing automatic weather stations for remote sites monitoring: From Niger to Nepal,” Green Energy Technol., vol. 0, no. 9783319590950, pp. 21–39, 2017. https://doi.org/10.1007/978-3-319-59096-7_2.

B. E. Demir, “A New Low-Cost Internet of Things-Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation,” Appl. Sci., vol. 13, no. 24, 2023. https://doi.org/10.3390/app132413072.

N. Rouibah et al., “Smart monitoring of photovoltaic energy systems: An IoT-based prototype approach,” Sci. African, vol. 30, no. May 2025, p. e02973, 2025. https://doi.org/10.1016/j.sciaf.2025.e02973.

M. J. B. Buni, A. A. K. Al-Walie, K. A. N. Al, and K. A. N. Al-Asadi, “Effect of solar radiation on photovoltaic cell,” ‖ Int. Res. J. Adv. Eng. Sci., vol. 3, no. 3, pp. 47–51, 2018. [Online]. Available: https://un.uobasrah.edu.iq/papers/4748.pdf.

S. Hegedus, Photovoltaic Science. England: John Wiley & Sons, 2003. https://doi.org/10.1002/0470014008.

Copernicus Atmosphere Monitoring Service, “CAMS solar radiation time-series,” Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store. Accessed: Jul. 06, 2025. [Online]. Available: https://ads.atmosphere.copernicus.eu/cams-solar-radiation-timeseries

M. Schroedter-Homscheidt et al., User Guide to the CAMS Radiation Service (CRS): Status April 2021, no. December. 2021. [Online]. Available: https://atmosphere.copernicus.eu/UserGuide_v1.pdf

M. Vivar, M. Fuentes, M. Norton, G. Makrides, and I. De Bustamante, “Estimation of sunshine duration from the global irradiance measured by a photovoltaic silicon solar cell,” Renew. Sustain. Energy Rev., vol. 36, pp. 26–33, 2014. https://doi.org/10.1016/j.rser.2014.04.045.