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An investigation of photovoltaic power forecasting in buildings considering shadow effects: Modeling approach and SHAP analysis

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  • Fu, Jiaqian
  • Sun, Yuying
  • Li, Yunhe
  • Wang, Wei
  • Wei, Wenzhe
  • Ren, Jinyang
  • Han, Shulun
  • Di, Haoran

Abstract

The power generation of distributed photovoltaic (PV) systems often suffers interference due to shadows cast by surrounding buildings. To improve the accuracy of PV power forecasts, this paper presents a PV power prediction method that takes shadow effects into consideration. Firstly, a convenient PV shadow model was formulated for predicting the proportion of PV shaded (PPS), using theoretical derivation and a zoning shading judgment strategy. Subsequently, a PV power prediction method was proposed based on PV shadow forecasting and the convolutional deep neural network algorithm. Finally, this method was applied to a carport PV system in a building in Beijing, China, and SHAP analysis was utilized for the interpretation. The results show that the proposed method can automatically recognize shadow conditions, and significantly improve the predictive accuracy of PV power, reducing the MAE by 10.1 % and increasing the R2 value from 0.91 to 0.94. The ranking of feature importance to the PV power prediction model is as follows: solar radiation, hour, ambient temperature, PPS, and relative humidity. This study offers a feasible solution for predicting power generation of PV systems that are subject to shadow shading from buildings.

Suggested Citation

  • Fu, Jiaqian & Sun, Yuying & Li, Yunhe & Wang, Wei & Wei, Wenzhe & Ren, Jinyang & Han, Shulun & Di, Haoran, 2025. "An investigation of photovoltaic power forecasting in buildings considering shadow effects: Modeling approach and SHAP analysis," Renewable Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:renene:v:245:y:2025:i:c:s0960148125004835
    DOI: 10.1016/j.renene.2025.122821
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    References listed on IDEAS

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    1. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    2. Keddouda, Abdelhak & Ihaddadene, Razika & Boukhari, Ali & Atia, Abdelmalek & Arıcı, Müslüm & Lebbihiat, Nacer & Ihaddadene, Nabila, 2024. "Experimentally validated thermal modeling for temperature prediction of photovoltaic modules under variable environmental conditions," Renewable Energy, Elsevier, vol. 231(C).
    3. Zhang, Yingbo & Tang, Hong & Li, Hangxin & Wang, Shengwei, 2025. "Unlocking the flexibilities of data centers for smart grid services: Optimal dispatch and design of energy storage systems under progressive loading," Energy, Elsevier, vol. 316(C).
    4. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    5. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    6. Gandoman, Foad H. & Raeisi, Fatima & Ahmadi, Abdollah, 2016. "A literature review on estimating of PV-array hourly power under cloudy weather conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 579-592.
    7. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    8. Yang, Mao & Jiang, Yue & Zhang, Wei & Li, Yi & Su, Xin, 2024. "Short-term interval prediction strategy of photovoltaic power based on meteorological reconstruction with spatiotemporal correlation and multi-factor interval constraints," Renewable Energy, Elsevier, vol. 237(PC).
    9. Alfredo Nespoli & Emanuele Ogliari & Sonia Leva & Alessandro Massi Pavan & Adel Mellit & Vanni Lughi & Alberto Dolara, 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques," Energies, MDPI, vol. 12(9), pages 1-15, April.
    10. Lu, Menglong & Sun, Yongjun & Ma, Zhenjun, 2024. "Multi-objective design optimization of multiple energy systems in net/nearly zero energy buildings under uncertainty correlations," Applied Energy, Elsevier, vol. 370(C).
    11. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).
    12. Zheng, Wanting & Xiao, Hao & Pei, Wei, 2025. "Distributed-regional photovoltaic power generation prediction with limited data: A robust autoregressive transfer learning method," Applied Energy, Elsevier, vol. 380(C).
    13. Li, Guozhu & Ding, Chenjun & Zhao, Naini & Wei, Jiaxing & Guo, Yang & Meng, Chong & Huang, Kailiang & Zhu, Rongxin, 2024. "Research on a novel photovoltaic power forecasting model based on parallel long and short-term time series network," Energy, Elsevier, vol. 293(C).
    14. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    15. Ren, Ye & Suganthan, P.N. & Srikanth, N., 2015. "Ensemble methods for wind and solar power forecasting—A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 82-91.
    16. Lian, Huihui & Ji, Ying & Niu, Menghan & Gu, Jiefan & Xie, Jingchao & Liu, Jiaping, 2025. "A hybrid load prediction method of office buildings based on physical simulation database and LightGBM algorithm," Applied Energy, Elsevier, vol. 377(PC).
    17. Chen, Congcong & Chai, Lin & Wang, Qingling, 2025. "Research on stacking ensemble method for day-ahead ultra-short-term prediction of photovoltaic power," Renewable Energy, Elsevier, vol. 238(C).
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    Cited by:

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    2. Zeng, Huanze & Shi, Chenlu & Fang, Haoyu & Wu, Binrong, 2025. "Interpretable multivariate wind speed forecasting using sliding masked window-based decomposition and deep autoregressive networks," Energy, Elsevier, vol. 341(C).
    3. Aiwen Shen & Yunqi Lin & Yiran Peng & KinTak U & Siyuan Zhao, 2025. "DSC-CBAM-BiLSTM: A Hybrid Deep Learning Framework for Robust Short-Term Photovoltaic Power Forecasting," Mathematics, MDPI, vol. 13(16), pages 1-15, August.
    4. Zeng, Huanze & Wu, Binrong & Fang, Haoyu & Lin, Jiacheng, 2025. "Interpretable wind speed forecasting through two-stage decomposition with comprehensive relative importance analysis," Applied Energy, Elsevier, vol. 392(C).
    5. Zhijian Hou & Yunhui Zhang & Xuemei Cheng & Xiaojiang Ye, 2025. "Photovoltaic Power Forecasting Based on Variational Mode Decomposition and Long Short-Term Memory Neural Network," Energies, MDPI, vol. 18(13), pages 1-28, July.

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