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Multiple operational strategies investigations of the PV/T collectors based on 3 days ahead hourly radiation prediction

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Listed:
  • Dong, Shiqian
  • Di, Yanqiang
  • Gao, Yafeng
  • Long, He
  • Fan, Zhixuan
  • Guan, Jingxuan
  • Han, Lijun
  • Wang, Yingming

Abstract

The PV/T collectors are the advanced photovoltaic and photothermal integration technology, realizing full-spectrum utilization. The energy profitability of PV/T collectors not only depends on sophisticated equipment but also significantly correlates with the operational conditioning. Hybrid operational strategies are analyzed with the WOA algorithm based on 3 days ahead hourly prediction model for horizontal total solar radiation. The 3-day variable inclination strategy is unsatisfactory. The monthly cumulative radiation increases by 8 % relatively with the ground reflectivity varying from 0.4 to 0.95. And, the 5 % addition of ground reflectivity leads to an average improvement of 0.45 %. The optimal long-side and wide-side ratios of the reflector are 0.9 and 0.6 with an improvement of 8.79 % and 3.85 % in thermal and electrical gain. Compared to fixed PV/T without a reflector, the multi-hybrid strategy can achieve a relative improvement of 17.76 % and 13.61 % in thermal and electrical gain for the novel PV/T with graphite and trapezoidal fluid channels, equivalent to 39.5 MJ and 7.8 kWh with a 2.08 m2 effective area. Finally, the best model is LSTM-Attention with the R2_train and R2_test of 96.8 % and 94.0 % for 3 days ahead hourly radiation prediction. The contribution of this paper provides references on the operational optimization of PV/T collectors, especially for household rural PV/T systems.

Suggested Citation

  • Dong, Shiqian & Di, Yanqiang & Gao, Yafeng & Long, He & Fan, Zhixuan & Guan, Jingxuan & Han, Lijun & Wang, Yingming, 2025. "Multiple operational strategies investigations of the PV/T collectors based on 3 days ahead hourly radiation prediction," Applied Energy, Elsevier, vol. 377(PA).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pa:s0306261924017665
    DOI: 10.1016/j.apenergy.2024.124383
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    as
    1. PraveenKumar, Seepana & Agyekum, Ephraim Bonah & Kumar, Abhinav & Velkin, Vladimir Ivanovich, 2023. "Performance evaluation with low-cost aluminum reflectors and phase change material integrated to solar PV modules using natural air convection: An experimental investigation," Energy, Elsevier, vol. 266(C).
    2. Sawadogo, Windmanagda & Bliefernicht, Jan & Fersch, Benjamin & Salack, Seyni & Guug, Samuel & Diallo, Belko & Ogunjobi, Kehinde.O. & Nakoulma, Guillaume & Tanu, Michael & Meilinger, Stefanie & Kunstma, 2023. "Hourly global horizontal irradiance over West Africa: A case study of one-year satellite- and reanalysis-derived estimates vs. in situ measurements," Renewable Energy, Elsevier, vol. 216(C).
    3. Lehna, Malte & Scheller, Fabian & Herwartz, Helmut, 2022. "Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account," Energy Economics, Elsevier, vol. 106(C).
    4. Inkawhich, Mikah & Shingler, Jeb & Ketchum, Remington S. & Pan, Wei & Norwood, Robert A. & Hickenbottom, Kerri L., 2023. "Temporal performance indicators for an integrated pilot-scale membrane distillation-concentrated solar power/photovoltaic system," Applied Energy, Elsevier, vol. 349(C).
    5. Shi, Hongrong & Yang, Dazhi & Wang, Wenting & Fu, Disong & Gao, Ling & Zhang, Jinqiang & Hu, Bo & Shan, Yunpeng & Zhang, Yingjie & Bian, Yuxuan & Chen, Hongbin & Xia, Xiangao, 2023. "First estimation of high-resolution solar photovoltaic resource maps over China with Fengyun-4A satellite and machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    6. Dong, Shiqian & Long, He & Guan, Jingxuan & Jiang, Lina & Zhuang, Chaoqun & Gao, Yafeng & Di, Yanqiang, 2024. "Performance investigation of a hybrid PV/T collector with a novel trapezoidal fluid channel," Energy, Elsevier, vol. 288(C).
    7. Agrawal, Monika & Chhajed, Priyank & Chowdhury, Amartya, 2022. "Performance analysis of photovoltaic module with reflector: Optimizing orientation with different tilt scenarios," Renewable Energy, Elsevier, vol. 186(C), pages 10-25.
    8. Liu, Fa & Wang, Xunming & Sun, Fubao & Wang, Hong, 2022. "Correct and remap solar radiation and photovoltaic power in China based on machine learning models," Applied Energy, Elsevier, vol. 312(C).
    9. Gupta, Priya & Singh, Rhythm, 2023. "Combining a deep learning model with multivariate empirical mode decomposition for hourly global horizontal irradiance forecasting," Renewable Energy, Elsevier, vol. 206(C), pages 908-927.
    10. Qiu, Rangjian & Li, Longan & Wu, Lifeng & Agathokleous, Evgenios & Liu, Chunwei & Zhang, Baozhong & Luo, Yufeng & Sun, Shanlei, 2022. "Modeling daily global solar radiation using only temperature data: Past, development, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    11. Naval, Natalia & Yusta, Jose M., 2022. "Comparative assessment of different solar tracking systems in the optimal management of PV-operated pumping stations," Renewable Energy, Elsevier, vol. 200(C), pages 931-941.
    12. Barthwal, Mohit & Rakshit, Dibakar, 2023. "A solar spectral splitting-based PVT collector with packed transparent tube receiver for co-generation of heat and electricity," Applied Energy, Elsevier, vol. 352(C).
    13. Liu, Yujun & Yao, Ling & Jiang, Hou & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2022. "Spatial estimation of the optimum PV tilt angles in China by incorporating ground with satellite data," Renewable Energy, Elsevier, vol. 189(C), pages 1249-1258.
    14. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-step solar irradiation prediction based on weather forecast and generative deep learning model," Renewable Energy, Elsevier, vol. 188(C), pages 637-650.
    15. Ju, Xing & Abd El-Samie, Mostafa M. & Xu, Chao & Yu, Hangyu & Pan, Xinyu & Yang, Yongping, 2020. "A fully coupled numerical simulation of a hybrid concentrated photovoltaic/thermal system that employs a therminol VP-1 based nanofluid as a spectral beam filter," Applied Energy, Elsevier, vol. 264(C).
    16. Elizabeth Michael, Neethu & Hasan, Shazia & Al-Durra, Ahmed & Mishra, Manohar, 2022. "Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network," Applied Energy, Elsevier, vol. 324(C).
    17. Wang, Gang & Zhang, Zhen & Chen, Zeshao, 2023. "Design and performance evaluation of a novel CPV-T system using nano-fluid spectrum filter and with high solar concentrating uniformity," Energy, Elsevier, vol. 267(C).
    18. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
    19. Zareie, Zahra & Ahmadi, Rouhollah & Asadi, Mahdi, 2024. "A comprehensive numerical investigation of a branch-inspired channel in roll-bond type PVT system using design of experiments approach," Energy, Elsevier, vol. 286(C).
    20. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    21. Kazemian, Arash & Khatibi, Meysam & Entezari, Soroush & Ma, Tao & Yang, Hongxing, 2023. "Efficient energy generation and thermal storage in a photovoltaic thermal system partially covered by solar cells and integrated with organic phase change materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    22. Acikgoz, Hakan, 2022. "A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting," Applied Energy, Elsevier, vol. 305(C).
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