Proof of Concept Digital Twin Berbasis Machine Learning untuk Prediksi dan Optimasi Kinerja Panel Surya

Ade Zulkarnain Hasibuan; Irwanda Syahputra

doi:10.35447/jitekh.v14i2.1605

Authors

Ade Zulkarnain Hasibuan Program Studi Informatika, Universitas Samudra, Langsa
Irwanda Syahputra Program Studi Informatika, Universitas Samudra, Langsa

DOI:

https://doi.org/10.35447/jitekh.v14i2.1605

Keywords:

Digital Twin, Machine Learning, Photovoltaic, Random Forest Regression, Angle Optimization

Abstract

This study developed and evaluated a machine learning–based Digital Twin proof of concept for a physical solar panel with a nominal capacity of 100 W. Environmental and electrical data were collected using sensors connected to an ESP32 and transmitted to a server for power prediction using Random Forest Regression. Exhaustive Scenario Search was used to compare panel angles of 25°, 35°, and 45°, while the dashboard presented measured power, predicted power, and angle recommendations. The analysis used 9,720 measurement records divided chronologically into training, validation, and testing datasets. Physical-panel validation was performed by comparing the predicted values with the measured power. For the 1,944 testing records, the model achieved an MAE of 2.769 W, an RMSE of 4.145 W, and an R² of 0.9446. The angle scenario evaluation produced 2,492.97 Wh of energy, which was 3.06% higher than the 2,418.91 Wh obtained with a fixed angle of 35°. These findings support the implementation of a Digital Twin proof of concept for panel-condition monitoring, power prediction, and angle recommendation under the tested conditions.

Downloads

Download data is not yet available.

References

X. Liu et al., “A systematic review of digital twin about physical entities, virtual models, twin data, and applications,” Advanced Engineering Informatics, vol. 55, p. 101876, Jan. 2023, doi: 10.1016/j.aei.2023.101876.

W. Kritzinger, M. Karner, G. Traar, J. Henjes, and W. Sihn, “Digital Twin in manufacturing: A categorical literature review and classification,” IFAC-PapersOnLine, vol. 51, no. 11, pp. 1016–1022, 2018, doi: 10.1016/j.ifacol.2018.08.474.

D. Jones, C. Snider, A. Nassehi, J. Yon, and B. Hicks, “Characterising the Digital Twin: A systematic literature review,” CIRP J. Manuf. Sci. Technol., vol. 29, pp. 36–52, May 2020, doi: 10.1016/j.cirpj.2020.02.002.

A. Elnosh, M. Calais, and D. Parlevliet, “A systematic literature review of digital twin research for photovoltaic systems: Trends, challenges, and opportunities,” Renewable and Sustainable Energy Reviews, vol. 226, p. 116326, Jan. 2026, doi: 10.1016/j.rser.2025.116326.

K. T. Alao, S. I. U. H. Gilani, K. Sopian, and T. O. Alao, “A review on digital twin application in photovoltaic energy systems: challenges and opportunities,” JMST Advances, vol. 6, no. 3, pp. 257–282, Sep. 2024, doi: 10.1007/s42791-024-00083-z.

I. Natgunanathan, V. Mak-Hau, S. Rajasegarar, and A. Anwar, “Deakin microgrid digital twin and analysis of AI models for power generation prediction,” Energy Conversion and Management: X, vol. 18, p. 100370, Apr. 2023, doi: 10.1016/j.ecmx.2023.100370.

X. Zhao, “A novel digital-twin approach based on transformer for photovoltaic power prediction,” Sci. Rep., vol. 14, no. 1, p. 26661, Nov. 2024, doi: 10.1038/s41598-024-76711-4.

K. Mahmud, S. Azam, A. Karim, S. Zobaed, B. Shanmugam, and D. Mathur, “Machine Learning Based PV Power Generation Forecasting in Alice Springs,” IEEE Access, vol. 9, pp. 46117–46128, 2021, doi: 10.1109/ACCESS.2021.3066494.

A. F. Amiri, A. Chouder, H. Oudira, S. Silvestre, and S. Kichou, “Improving Photovoltaic Power Prediction: Insights through Computational Modeling and Feature Selection,” Energies (Basel)., vol. 17, no. 13, p. 3078, Jun. 2024, doi: 10.3390/en17133078.

L. Grinsztajn, E. Oyallon, and G. Varoquaux, “Why Do Tree-Based Models Still Outperform Deep Learning on Typical Tabular Data?,” in Advances in Neural Information Processing Systems 35, San Diego, California, USA: Neural Information Processing Systems Foundation, Inc. (NeurIPS), 2022, pp. 507–520. doi: 10.52202/068431-0037.

G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, An Introduction to Statistical Learning. Cham: Springer International Publishing, 2023. doi: 10.1007/978-3-031-38747-0.

D. R. Roberts et al., “Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure,” Ecography, vol. 40, no. 8, pp. 913–929, Aug. 2017, doi: 10.1111/ecog.02881.

V. Cerqueira, L. Torgo, and I. Mozetič, “Evaluating time series forecasting models: an empirical study on performance estimation methods,” Mach. Learn., vol. 109, no. 11, pp. 1997–2028, Nov. 2020, doi: 10.1007/s10994-020-05910-7.

S. Kapoor and A. Narayanan, “Leakage and the reproducibility crisis in machine-learning-based science,” Patterns, vol. 4, no. 9, p. 100804, Sep. 2023, doi: 10.1016/j.patter.2023.100804.

R. A. . Messenger and Amir. Abtahi, Photovoltaic systems engineering. CRC Press, Taylor & Francis Group, CRC Press is an imprint of the Taylor & Francis Group, an informa business, 2017.

P. Donti, B. Amos, and J. Z. Kolter, “Task-based End-to-end Model Learning in Stochastic Optimization,” in Advances in Neural Information Processing Systems, 2017, pp. 5484–5494.

D. K. M. Idris, C. S. Marțiș, R. Birasa, F. naziri Moghaddam, and D. R. Philippe, “From Architecture to Action: Implementing and Validating a Digital Twin for PV Systems Using Real-Time IoT Data,” in 2025 International Conference on Future Energy Solutions (FES), IEEE, Sep. 2025, pp. 1–6. doi: 10.1109/FES65767.2025.11341797.

L. Chalal, A. Saadane, and A. Rachid, “Unified Environment for Real Time Control of Hybrid Energy System Using Digital Twin and IoT Approach,” Sensors, vol. 23, no. 12, p. 5646, Jun. 2023, doi: 10.3390/s23125646.

P. J. Hueros-Barrios, F. J. R. Sánchez, M. Tradacete-ágreda, P. Martín, C. S. Pérez, and D. Perez-Saura, “A low-cost digital twin for real-time monitoring of photovoltaic panels,” in 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), IEEE, Jun. 2023, pp. 1–6. doi: 10.1109/ISIE51358.2023.10228172.

C. Scott, M. Ahsan, and A. Albarbar, “Machine learning for forecasting a photovoltaic (PV) generation system,” Energy, vol. 278, p. 127807, Sep. 2023, doi: 10.1016/j.energy.2023.127807.

M. Massaoudi, I. Chihi, L. Sidhom, M. Trabelsi, S. S. Refaat, and F. S. Oueslati, “Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements,” Energies (Basel)., vol. 14, no. 13, p. 3992, Jul. 2021, doi: 10.3390/en14133992.

D. Rangelov, M. Boerger, N. Tcholtchev, P. Lämmel, and M. Hauswirth, “Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features,” IEEE Access, vol. 11, pp. 41578–41595, 2023, doi: 10.1109/ACCESS.2023.3270714.

D. Liu and K. Sun, “Random forest solar power forecast based on classification optimization,” Energy, vol. 187, p. 115940, Nov. 2019, doi: 10.1016/j.energy.2019.115940.

D. Niu, K. Wang, L. Sun, J. Wu, and X. Xu, “Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study,” Appl. Soft Comput., vol. 93, p. 106389, Aug. 2020, doi: 10.1016/j.asoc.2020.106389.

O. Z. Lin, L. Štěpanec, E. Koutroulis, D. Juchelkova, and H. Y. Aye, “Optimizing monthly solar PV tilt angles and energy yield across global climate zones: A hybrid machine learning and PVLib approach,” Renew. Energy, vol. 260, p. 125163, Mar. 2026, doi: 10.1016/j.renene.2025.125163.