Forecasting Photovoltaic Performance: A Comparative Assessment of Machine Learning Methods

Ayman Mdallal

doi:10.53941/rest.2026.100001

Abstract

The increasing global need for renewable energy sources has identified photovoltaic systems as essential to clean energy transition initiatives. This study examines the predictive reliability of machine learning models in assessing and forecasting the output and thermal performance of a medium-scale photovoltaic facility, employing both monofacial and bifacial modules in Canada. A simulation model was created utilizing the System Advisor Model (SAM) with five years of weather data to produce hourly outputs, including power and photovoltaic cell temperature. The datasets were analyzed utilizing multiple regression-based machine learning algorithms, such as Linear Regression, Polynomial Regression, Decision Tree, Random Forest, XGBoost, and regularization methods. Key performance indicators, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²), were assessed to compare model accuracy. Results demonstrate substantial enhancements in prediction accuracy through the utilization of ensemble models such as Random Forest and XGBoost, attaining R² values over 97% for both power output and cell temperature forecasts. Bifacial systems exhibited superior energy generation efficiency and tolerance to thermal fluctuations relative to monofacial systems, owing to their dual-sided light capture capability. Analysis of feature importance indicated that Global Horizontal Irradiance (GHI), temperature, and wind speed were the primary determinants affecting performance.

References

1.
Maka, A.O.M.; Alabid, J.M. Solar Energy Technology and Its Roles in Sustainable Development. Clean Energy 2022, 6, 476–483. https://doi.org/10.1093/ce/zkac023.
2.
Anonymous. Clean Energy Can Fuel the Future—And Make the World Healthier. Nature 2023, 620, 245. https://doi.org/10.1038/d41586-023-02510-y.
3.
Lazaroiu, A.C.; Gmal Osman, M.; Strejoiu, C.V.; et al. A Comprehensive Overview of Photovoltaic Technologies and Their Efficiency for Climate Neutrality. Sustainability 2023, 15, 16297. https://doi.org/10.3390/su152316297.
4.
Sayed, E.T.; Olabi, A.G.; Alami, A.H.; et al. Renewable Energy and Energy Storage Systems. Energies 2023, 16, 1415. https://doi.org/10.3390/en16031415.
5.
Garrod, A.; Ghosh, A. A Review of Bifacial Solar Photovoltaic Applications. Front. Energy 2023, 17, 704–726. https://doi.org/10.1007/s11708-023-0903-7.
6.
Badran, G.; Dhimish, M. A Comparative Study of Bifacial versus Monofacial PV Systems at the UK’s Largest Solar Plant. Clean Energy 2024, 8, 248–260. https://doi.org/10.1093/ce/zkae043.
7.
James, A. Solar Photovoltaic Energy: Advantages and Disadvantages. Glob. J. Eng. Archit. 2021, 2, 1–2.
8.
Soomar, A.M.; Hakeem, A.; Messaoudi, M.; et al. Solar Photovoltaic Energy Optimization and Challenges. Front. Energy Res. 2022, 10, 879985. https://doi.org/10.3389/fenrg.2022.879985.
9.
Mohammadi, F.; Neagoe, M. Emerging Issues and Challenges with the Integration of Solar Power Plants into Power Systems. In Proceedings of the 2020 Conference for Sustainable Energy (CSE), Brasov, Romania, 22–24 October 2020; pp. 157–173. https://doi.org/10.1007/978-3-030-55757-7_11.
10.
Shaker, L.M.; Al-Amiery, A.A.; Hanoon, M.M.; et al. Examining the Influence of Thermal Effects on Solar Cells: A Comprehensive Review. Sustain. Energy Res. 2024, 11, 1–30. https://doi.org/10.1186/s40807-024-00100-8.
11.
Sun, C.; Zou, Y.; Qin, C.; et al. Temperature Effect of Photovoltaic Cells: A Review. Adv. Compos. Hybrid Mater. 2022, 5, 2675–2699. https://doi.org/10.1007/s42114-022-00533-z.
12.
Forootan, M.M.; Larki, I.; Zahedi, R.; et al. Machine Learning and Deep Learning in Energy Systems: A Review. Sustainability 2022, 14, 4832. https://doi.org/10.3390/su14084832.
13.
Ukoba, K.; Onisuru, O.R.; Jen, T.-C. Harnessing Machine Learning for Sustainable Futures: Advancements in Renewable Energy and Climate Change Mitigation. Bull. Natl. Res. Cent. 2024, 48, 1–15. https://doi.org/10.1186/s42269-024-01254-7.
14.
Yao, Z.; Lum, Y.; Johnston, A.; et al. Machine Learning for a Sustainable Energy Future. Nat. Rev. Mater. 2023, 8, 202–215. https://doi.org/10.1038/s41578-022-00490-5.
15.
Al-Saban, O.; Alkadi, M.; Qaid, S.M.H.; et al. Machine Learning Algorithms in Photovoltaics: Evaluating Accuracy and Computational Cost Across Datasets of Different Generations, Sizes, and Complexities. J. Electron. Mater. 2024, 53, 1530–1538. https://doi.org/10.1007/s11664-023-10897-7.
16.
Tina, G.M.; Ventura, C.; Ferlito, S.; et al. A State-of-Art-Review on Machine-Learning Based Methods for PV. Appl. Sci. 2021, 11, 7550. https://doi.org/10.3390/app11167550.
17.
Mohamad Radzi, P.N.L.; Akhter, M.N.; Mekhilef, S.; et al. Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting. Sustainability 2023, 15, 2942. https://doi.org/10.3390/su15042942.
18.
Gaboitaolelwe, J.; Zungeru, A.M.; Yahya, A.; et al. Machine Learning Based Solar Photovoltaic Power Forecasting: A Review and Comparison. IEEE Access 2023, 11, 40820–40845. https://doi.org/10.1109/ACCESS.2023.3270041.
19.
Benitez, S.; Benitez, I.B.; Singh, J.G. A Comprehensive Review of Machine Learning Applications in Forecasting Solar PV and Wind Turbine Power Output. J. Electr. Syst. Inf. Technol. 2025, 12, 54. https://doi.org/10.1186/s43067-025-00239-4.
20.
Hong, D.; Ma, J.; Wang, K.; et al. Real-Time Power Prediction for Bifacial PV Systems in Varied Shading Conditions: A Circuit-LSTM Approach Within a Digital Twin Framework. IEEE J. Photovolt. 2024, 14, 652–660. https://doi.org/10.1109/JPHOTOV.2024.3393001.
21.
Grisanti, M.; Mannino, G.; Tina, G.M.; et al. Thermal Models of Monofacial and Bifacial PV Modules: Machine Learning and Physical Estimation Models Comparison. In Proceedings of the 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), San Juan, PR, USA, 11–16 June 2023. https://doi.org/10.1109/PVSC48320.2023.10360013.
22.
Shi, Y.; Zhang, L.; Wang, S.; et al. A Short-Term Photovoltaic Power Interval Forecasting Method Based on Fuzzy Granular Computing and CNN-BiGRU. Int. J. Low-Carbon Technol. 2024, 19, 306–314. https://doi.org/10.1093/ijlct/ctad131.
23.
Said, N.M.; Suleiman, R.F.R.; Rahim, N.H.A.; et al. Short-Term Photovoltaic (PV) Energy Prediction Using Machine Learning Approach. In Springer Briefs in Applied Sciences and Technology; Springer Nature Switzerland, 2024; pp. 111–118. https://doi.org/10.1007/978-3-031-63326-3_14.
24.
Souhe, F.G.Y.; Mbey, C.F.; Kakeu, V.J.F.; et al. Optimized Forecasting of Photovoltaic Power Generation Using Hybrid Deep Learning Model Based on GRU and SVM. Electr. Eng. 2024, 106, 7879–7898. https://doi.org/10.1007/s00202-024-02492-8.
25.
Miraftabzadeh, S.M.; Colombo, C.G.; Longo, M.; et al. A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks. Forecasting 2023, 5, 213–228. https://doi.org/10.3390/forecast5010012.
26.
Zhang, C.; Fu, X.; Li, Z.; et al. Unified Fourier Graph-Based Spatiotemporal Learning and Corrected NWP for Multi-Site Ultra-Short Term Photovoltaic Power Forecasting. IEEE Trans. Smart Grid 2025, 17, 1639–1652. https://doi.org/10.1109/TSG.2025.3628129.
27.
Fu, X.; Chang, F.; Li, Z.; et al. Redesigning the Decoder and Loss Function of Diffusion Transformer for PV Temporal Simulation. IEEE Trans. Smart Grid 2025, 17, 1629–1638. https://doi.org/10.1109/TSG.2025.3627923.
28.
Psomopoulos, C.S.; Ioannidis, G.C.; Kaminaris, S.D.; et al. A Comparative Evaluation of Photovoltaic Electricity Production Assessment Software (PVGIS, PVWatts and RETScreen). Environ. Process. 2015, 2, S175–S189. https://doi.org/10.1007/s40710-015-0092-4.
29.
Maulud, D.; Abdulazeez, A.M. A Review on Linear Regression Comprehensive in Machine Learning. J. Appl. Sci. Technol. Trends 2020, 1, 140–147. https://doi.org/10.38094/jastt1457.
30.
Belany, P.; Hrabovsky, P.; Sedivy, S.; et al. A Comparative Analysis of Polynomial Regression and Artificial Neural Networks for Prediction of Lighting Consumption. Buildings 2024, 14, 1712. https://doi.org/10.3390/buildings14061712.
31.
De Vlaming, R.; Groenen, P.J.F. The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics. BioMed Res. Int. 2015, 2015, 143712. https://doi.org/10.1155/2015/143712.
32.
Li, X.; Wang, Y.; Ruiz, R. A Survey on Sparse Learning Models for Feature Selection. IEEE Trans. Cybern. 2022, 52, 1642–1660. https://doi.org/10.1109/TCYB.2020.2982445.
33.
Hajihosseinlou, M.; Maghsoudi, A.; Ghezelbash, R. Regularization in Machine Learning Models for MVT Pb-Zn Prospectivity Mapping: Applying Lasso and Elastic-Net Algorithms. Earth Sci. Inform. 2024, 17, 4859–4873. https://doi.org/10.1007/s12145-024-01404-5.
34.
Harati, S.; Gomari, S.R.; Rahman, M.A.; et al. Performance Analysis of Various Machine Learning Algorithms for CO₂ Leak Prediction and Characterization in Geo-Sequestration Injection Wells. Process Saf. Environ. Prot. 2024, 183, 99–110. https://doi.org/10.1016/j.psep.2024.01.007.
35.
Singh Kushwah, J.; Kumar, A.; Patel, S.; et al. Comparative Study of Regressor and Classifier with Decision Tree Using Modern Tools. Mater. Today Proc. 2022, 56, 3571–3576. https://doi.org/10.1016/j.matpr.2021.11.635.
36.
Strobl, C.; Malley, J.; Tutz, G. An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests. Psychol. Methods 2009, 14, 323–348. https://doi.org/10.1037/a0016973.
37.
Sagi, O.; Rokach, L. Approximating XGBoost with an Interpretable Decision Tree. Inf. Sci. 2021, 572, 522–542. https://doi.org/10.1016/j.ins.2021.05.055.
38.
Deline, C.; Ayala Peláez, S.; Marion, B.; et al. Bifacial PV System Performance: Separating Fact from Fiction; National Renewable Energy Laboratory: Chicago, IL, USA, 2019.
39.
Alam, M.; Gul, M.S.; Muneer, T. Performance Analysis and Comparison Between Bifacial and Monofacial Solar Photovoltaic at Various Ground Albedo Conditions. Renew. Energy Focus 2023, 44, 295–316. https://doi.org/10.1016/j.ref.2023.01.005.
40.
Rodríguez-Gallegos, C.D.; Bieri, M.; Gandhi, O.; et al. Monofacial vs. Bifacial Si-Based PV Modules: Which One Is More Cost-Effective? Sol. Energy 2018, 176, 412–438. https://doi.org/10.1016/j.solener.2018.10.012.
41.
Lazard 2023 Levelized Cost of Energy+. Available online: https://www.lazard.com/research-insights/2023-levelized-cost-of-energyplus/?utm_source=chatgpt.com (accessed on 5 February 2026).
42.
Annual Planning Outlook. Available online: https://www.ieso.ca/Sector-Participants/Engagement-Initiatives/Engagements/Annual-Planning-Outlook (accessed on 5 February 2026).

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