A BENCHMARK MODEL FOR SOLAR IRRADIANCE FORECASTING： ATMOSPHERIC TRANSPARENCY FACTOR PERSISTENT MODEL

Xu Ximeng; Ma Chao; Deng Zexing; Liu Run

doi:10.19912/j.0254-0096.tynxb.2025-1126

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (12) : 198-204. DOI: 10.19912/j.0254-0096.tynxb.2025-1126

A BENCHMARK MODEL FOR SOLAR IRRADIANCE FORECASTING： ATMOSPHERIC TRANSPARENCY FACTOR PERSISTENT MODEL

Xu Ximeng^1,2, Ma Chao^1,2, Deng Zexing^1,2, Liu Run^1,2

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Abstract

To address the issue of inadequate and inaccurate physical information guidance in solar irradiance forecast models, an atmospheric transparency factor persistent model （ATF-P） is proposed. After accurately modeling the solar irradiance propagation process, the deterministic and stochastic components in the solar irradiance time series are decoupled, and the steady atmospheric transparency factor is extracted as the key variable to construct a persistent forecast model. A case study using solar irradiance data from a location in Sichuan province shows that the root mean square error （RMSE） metric of the proposed ATF-P model is 55.59 W/m². Compared with other models, it has stronger consistency with physical laws, higher forecast accuracy, and simpler modeling costs, making it suitable for promotion and application as a new benchmark model.

Key words

solar irradiance / forecast / atmospheric transparency factor / benchmark model / persistent model / clear sky model

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Xu Ximeng, Ma Chao, Deng Zexing, Liu Run. A BENCHMARK MODEL FOR SOLAR IRRADIANCE FORECASTING： ATMOSPHERIC TRANSPARENCY FACTOR PERSISTENT MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(12): 198-204 https://doi.org/10.19912/j.0254-0096.tynxb.2025-1126

References

[1] Wang J X, Chen L D, Tan Z F, et al.Inherent spatiotemporal uncertainty of renewable power in China[J]. Nature Communications, 2023, 14: 5379.
[2] Gandhi O, Zhang W J, Kumar D S, et al.The value of solar forecasts and the cost of their errors: a review[J]. Renewable and Sustainable Energy Reviews, 2024, 189: 113915.
[3] Tsai W C, Tu C S, Hong C M, et al.A review of state-of-the-art and short-term forecasting models for solar PV power generation[J]. Energies, 2023, 16(14): 5436.
[4] 倪超, 王聪, 朱婷婷, 等. 基于CNN-Bi-LSTM的太阳辐照度超短期预测[J]. 太阳能学报, 2022, 43(3): 197-202.
Ni C, Wang C, Zhu T T, et al.Super-short-term forecast of solar irradiance based on CNN-Bi-LSTM[J]. Acta Energiae Solaris Sinica, 2022, 43(3): 197-202.
[5] 臧海祥, 张越, 程礼临, 等. 基于ICEEMDAN-LSTM和残差注意力的短期太阳辐照度预测[J]. 太阳能学报, 2023, 44(12): 175-181.
Zang H X, Zhang Y, Cheng L L, et al.Short term solar radiation forecasting based on ICEEMDAN-LSTM and residual attention[J]. Acta Energiae Solaris Sinica, 2023, 44(12): 175-181.
[6] 周满国, 黄艳国, 段锦锋. 基于GRU-RF模型的太阳辐照度短时预测[J]. 太阳能学报, 2022, 43(7): 166-173.
Zhou M G, Huang Y G, Duan J F.Short term prediction of solar irradiance based on GRU-RF model[J]. Acta Energiae Solaris Sinica, 2022, 43(7): 166-173.
[7] Karpatne A, Atluri G, Faghmous J H, et al.Theory-guided data science: a new paradigm for scientific discovery from data[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10): 2318-2331.
[8] Lauret P, Alonso-Suárez R, Le Gal La Salle J, et al. Solar forecasts based on the clear sky index or the clearness index: which is better?[J]. Solar, 2022, 2(4): 432-444.
[9] Li Y, Zhou W S, Wang Y F, et al.Interpretable deep learning framework for hourly solar radiation forecasting based on decomposing multi-scale variations[J]. Applied Energy, 2025, 377: 124409.
[10] Nie Y H, Li X T, Paletta Q, et al.Open-source sky image datasets for solar forecasting with deep learning: a comprehensive survey[J]. Renewable and Sustainable Energy Reviews, 2024, 189: 113977.
[11] Spencer J W.Fourier series representation of the position of the Sun[J]. Search, 1971, 2: 172.
[12] Fröhlich C, Brusa R W.Solar radiation and its variation in time[J]. Solar Physics, 1981, 74(1): 209-215.
[13] Rapti A S.Atmospheric transparency, atmospheric turbidity and climatic parameters[J]. Solar Energy, 2000, 69(2): 99-111.
[14] Yang D Z, Alessandrini S, Antonanzas J, et al.Verification of deterministic solar forecasts[J]. Solar Energy, 2020, 210: 20-37.
[15] Reno M J, Hansen C W, Stein J S.Global horizontal irradiance clear sky models: implementation and analysis: SAND2012-2389[R]. Sandia National Laboratories, 2012.
[16] Inman R H, Pedro H T C, Coimbra C F M. Solar forecasting methods for renewable energy integration[J]. Progress in Energy and Combustion Science, 2013, 39(6): 535-576.
[17] van der Meer D W, Widén J, Munkhammar J. Review on probabilistic forecasting of photovoltaic power production and electricity consumption[J]. Renewable and Sustainable Energy Reviews, 2018, 81: 1484-1512.