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Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power

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  • Ramadhan, Raden A.A.
  • Heatubun, Yosca R.J.
  • Tan, Sek F.
  • Lee, Hyun-Jin

Abstract

Conventional models to estimate solar irradiance and photovoltaic power rely on physics and use empirical correlations to handle regional climate and complex physics. Recently, machine learning emerges as an advanced statistical tool to construct more accurate correlations between inputs and outputs. Although machine learning has been applied for modeling solar irradiance and power, no study has reported the accuracy improvement by machine learning compared to conventional physical models. Hence, this study aims to compare the accuracies of physical and machine learning models at each step of solar power modeling, i.e., modeling of global horizontal irradiance, direct normal irradiance, global tilted irradiance, and photovoltaic power. Comparison results demonstrated that machine learning models generally outperform physical models when input parameters are appropriately selected. Machine learning models more significantly reduced the mean bias difference (MBD) than the root mean square difference (RMSD). For global horizontal irradiance and photovoltaic power, machine learning models led to substantially unbiased estimations with 0.96% and 0.03% of MBD, respectively. Among machine learning algorithms, long short-term memory and gated recurrent unit were more recommendable. However, the physical model for solar power estimation was more efficient to reduce RMSD because of their ability to consider constant parameters as input.

Suggested Citation

  • Ramadhan, Raden A.A. & Heatubun, Yosca R.J. & Tan, Sek F. & Lee, Hyun-Jin, 2021. "Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power," Renewable Energy, Elsevier, vol. 178(C), pages 1006-1019.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:1006-1019
    DOI: 10.1016/j.renene.2021.06.079
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    5. Raihan Kamil & Pranda M. P. Garniwa & Hyunjin Lee, 2021. "Performance Assessment of Global Horizontal Irradiance Models in All-Sky Conditions," Energies, MDPI, vol. 14(23), pages 1-20, November.
    6. Zhang, Yanyun & Xue, Peng & Zhao, Yifan & Zhang, Qianqian & Bai, Gongxun & Peng, Jinqing & Li, Bojia, 2024. "Spectra measurement and clustering analysis of global horizontal irradiance for solar energy application," Renewable Energy, Elsevier, vol. 222(C).
    7. Jinfeng Wang & Wenshan Hu & Lingfeng Xuan & Feiwu He & Chaojie Zhong & Guowei Guo, 2024. "TransPVP: A Transformer-Based Method for Ultra-Short-Term Photovoltaic Power Forecasting," Energies, MDPI, vol. 17(17), pages 1-19, September.
    8. Qiu, Lihong & Ma, Wentao & Feng, Xiaoyang & Dai, Jiahui & Dong, Yuzhuo & Duan, Jiandong & Chen, Badong, 2024. "A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique," Applied Energy, Elsevier, vol. 359(C).
    9. Bo Gu & Xi Li & Fengliang Xu & Xiaopeng Yang & Fayi Wang & Pengzhan Wang, 2023. "Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    10. Garniwa, Pranda M.P. & Lee, Hyunjin, 2023. "Intercomparison of the parameterized Linke turbidity factor in deriving global horizontal irradiance," Renewable Energy, Elsevier, vol. 212(C), pages 285-298.
    11. Adnan Aslam & Naseer Ahmed & Safian Ahmed Qureshi & Mohsen Assadi & Naveed Ahmed, 2022. "Advances in Solar PV Systems; A Comprehensive Review of PV Performance, Influencing Factors, and Mitigation Techniques," Energies, MDPI, vol. 15(20), pages 1-52, October.

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