A Study on Novel Solar Power Forecasting using an XGB-LiGBM-RF Hybrid Model and the L-BFGS-B Optimization Algorithm
Received: 5 April 2025 | Revised: 7 May 2025 | Accepted: 17 May 2025 | Online: 20 June 2025
Corresponding author: Manh Hai Pham
Abstract
Accurate forecasting of solar power is essential for enhancing the stability and efficiency of power systems with high Photovoltaic (PV) penetration. This paper proposes a novel hybrid model based on a Stacking Ensemble (SE) of XGBoost, LightGBM, and Random Forest (RF), with optimal weights determined using the Limited Memory Broyden–Fletcher Goldfarb Shanno with Box constraints (L-BFGS-B) algorithm. The model is trained and tested on real-world data from a 49.5 MW solar power plant in Vietnam. The experimental results show that the proposed SE-XGB-LGBM-RF-OW model outperforms individual learners and deep learning baselines in both accuracy and training time. It consistently achieves a Normalized Mean Absolute Percentage Error (NMAPE) below 1.2% across all seasons. Compared to LSTM and GRU models, SE reduces Root Mean Square Error (RMSE) by more than 90% and shortens training time by over 20 times. Additionally, it significantly lowers the MAPE and NMAPE values, with improvements exceeding 90% in most seasonal test cases, highlighting the model’s superior accuracy and generalization capability.
Keywords:
forecasting, solar power, stacking ensemble model, XGBoost, LightGBM, random forestDownloads
References
K. Wang, X. Qi, and H. Liu, "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, vol. 251, Oct. 2019, Art. no. 113315.
L. Liu et al., "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, vol. 228, pp. 700–711, Oct. 2018.
M. Aslam, S.-J. Lee, S.-H. Khang, and S. Hong, "Two-Stage Attention Over LSTM With Bayesian Optimization for Day-Ahead Solar Power Forecasting," IEEE Access, vol. 9, pp. 107387–107398, Jul. 2021.
D. W. van der Meer, M. Shepero, A. Svensson, J. Widén, and J. Munkhammar, "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes," Applied Energy, vol. 213, pp. 195–207, Mar. 2018.
H. Sangrody, N. Zhou, and Z. Zhang, "Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting," IEEE Access, vol. 8, pp. 104469–104478, Jun. 2020.
J. Gaboitaolelwe, A. M. Zungeru, A. Yahya, C. K. Lebekwe, D. N. Vinod, and A. O. Salau, "Machine Learning Based Solar Photovoltaic Power Forecasting: A Review and Comparison," IEEE Access, vol. 11, pp. 40820–40845, Apr. 2023.
.Wang, P. Li, R. Ran, Y. Che, and Y. Zhou, "A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree," Applied Sciences, vol. 8, no. 5, May. 2018, Art. no. 689.
C. Saigustia and P. Pijarski, "Time Series Analysis and Forecasting of Solar Generation in Spain Using eXtreme Gradient Boosting: A Machine Learning Approach," Energies, vol. 16, no. 22, Jan. 2023, Art. no. 7618.
A. Abdellatif et al., "Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model," Sustainability, vol. 14, no. 17, Jan. 2022, Art. no 11083.
W. Khan, S. Walker, and W. Zeiler, "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, vol. 240, Feb. 2022, Art. no. 122812.
N. Fraccanabbia, R. G. da Silva, M. Henrique Dal Molin Ribeiro, S. R. Moreno, L. dos Santos Coelho, and V. C. Mariani, "Solar Power Forecasting Based on Ensemble Learning Methods," in 2020 International Joint Conference on Neural Networks (IJCNN)., Glasgow, UK, 2020, pp. 1-7.
R. Abbassi, S. Saidi, A. Abbassi, H. Jerbi, M. Kchaou, and B. N. Alhasnawi, "Accurate Key Parameters Estimation of PEMFCs’ Models Based on Dandelion Optimization Algorithm, " Mathematics, vol. 11, no. 6, Jan. 2023, Art. no. 1298.
A. Abbassi et al., "Improved Arithmetic Optimization Algorithm for Parameters Extraction of Photovoltaic Solar Cell Single-Diode Model," Arabian Journal for Science and Engineering, vol. 47, no. 8, pp. 10435–10451, Aug. 2022.
C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal, "Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization," ACM Transactions on Mathematical Software, vol. 23, no. 4, pp. 550–560, Sep. 1997.
T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining., New York, NY, USA, 2016, pp. 785–794.
G. Ke et al, "LightGBM: a highly efficient gradient boosting decision tree," in Proceedings of the 31st International Conference on Neural Information Processing Systems., Red Hook, NY, USA, 2017, pp. 3149–3157.
L. Breiman, "Random Forests,"Machine Learning, vol. 45, no. 1, pp. 5–32, Jul. 2001.
Y. Kassem, H. Camur, M. T. Adamu, T. Chikowero, and T. Apreala, "Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11472–11483, Aug. 2023.
Standard Process of Renewable Energy Forecasting, Decision 67/QĐ-ĐTĐL, Ministry of Industry and Trade (MOIT), Vietnam, 2020.
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Copyright (c) 2025 Tuan Anh Nguyen, Manh Hai Pham, Minh Phap Vu, Ngoc Trung Nguyen, Dang Toan Nguyen, Thi Anh Tho Vu, Trong Tuan Tran, Anh Tuan Do
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