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Short-term photovoltaic power prediction model based on feature construction and improved transformer

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  • Tang, Huadu
  • Kang, Fei
  • Li, Xinyu
  • Sun, Yong

Abstract

The accurate predictions of photovoltaic (PV) power generation are crucial for the efficient operation, management, and dispatch of power systems. This study presents a short-term PV hybrid forecasting model that leverages feature construction and an improved Transformer architecture. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is optimized using the FOX optimization method to obtain smooth intrinsic modal function (IMF), which are combined with meteorological data to extract key features. Furthermore, a PV power prediction model is constructed based on an iTransformer network, and an attention mechanism is incorporated to capture frequency information between multivariate features, thereby enhancing the capability of prediction. To validate the effectiveness of the proposed method, experiments were conducted using actual PV data from the desert knowledge Australia solar centre (DKASC). The results indicate that the prediction performance of the proposed method is improved, with a root mean square error (RMSE) of 0.449 kW and a mean absolute error (MAE) of 0.333 kW. Compared to the iTransformer model, the proposed method achieves improvements in RMSE and MAE by 25.6 % and 17.7 %, respectively. The application of the proposed model to different PV generation data from DKASC demonstrates its accuracy, applicability, and usefulness.

Suggested Citation

  • Tang, Huadu & Kang, Fei & Li, Xinyu & Sun, Yong, 2025. "Short-term photovoltaic power prediction model based on feature construction and improved transformer," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225008552
    DOI: 10.1016/j.energy.2025.135213
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    1. van der Meer, D.W. & Shepero, M. & Svensson, A. & Widén, J. & Munkhammar, J., 2018. "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes," Applied Energy, Elsevier, vol. 213(C), pages 195-207.
    2. Yang Li & Feng Ye & Zihao Liu & Zhijian Wang & Yupeng Mao, 2021. "A Short-Term Photovoltaic Power Generation Forecast Method Based on LSTM," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, January.
    3. Yusen Wang & Wenlong Liao & Yuqing Chang, 2018. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting," Energies, MDPI, vol. 11(8), pages 1-14, August.
    4. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2021. "Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge," Energy, Elsevier, vol. 225(C).
    5. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    6. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    7. Zheng, Lingwei & Liu, Zhaokun & Shen, Junnan & Wu, Chenxi, 2018. "Very short-term maximum Lyapunov exponent forecasting tool for distributed photovoltaic output," Applied Energy, Elsevier, vol. 229(C), pages 1128-1139.
    8. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    9. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
    10. Moreira, M.O. & Balestrassi, P.P. & Paiva, A.P. & Ribeiro, P.F. & Bonatto, B.D., 2021. "Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    12. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    13. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    14. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    15. Wang, Xinyu & Ma, Wenping, 2024. "A hybrid deep learning model with an optimal strategy based on improved VMD and transformer for short-term photovoltaic power forecasting," Energy, Elsevier, vol. 295(C).
    16. Yin, Wansi & Han, Yutong & Zhou, Hai & Ma, Ming & Li, Li & Zhu, Honglu, 2020. "A novel non-iterative correction method for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 159(C), pages 23-32.
    17. Li, Zhengmao & Wu, Lei & Xu, Yan & Wang, Luhao & Yang, Nan, 2023. "Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids," Applied Energy, Elsevier, vol. 331(C).
    18. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    19. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
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    2. Hou, Guolian & Ye, Lingling & Cao, Huan, 2025. "Data-driven wide-load modeling and electricity-heat coordinated control for the supercritical combined heat and power unit," Energy, Elsevier, vol. 332(C).
    3. Li, Yanmei & Zhang, Yi & Yin, Minghao, 2026. "Physics-informed Mamba network for ultra-short-term photovoltaic power forecasting: integrating WGAN-GP augmentation and CEEMDAN-SST decomposition," Renewable Energy, Elsevier, vol. 257(C).
    4. 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.
    5. Ridha, Hussein Mohammed & Ahmadipour, Masoud & Alghrairi, Mokhalad & Hizam, Hashim & Mirjalili, Seyedali & Zubaidi, Salah L. & Mohammed S, Marwa Y., 2026. "A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive beluga whale optimization, and improved extreme learning machine," Renewable Energy, Elsevier, vol. 256(PA).
    6. Yongmei Ding & Shangnan Zhou & Wenwu Deng, 2025. "Sustainable PV Power Forecasting via MPA-VMD Optimized BiGRU with Attention Mechanism," Mathematics, MDPI, vol. 13(9), pages 1-26, May.

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