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DTCformer: A Temporal Convolution-Enhanced Autoformer with DILATE Loss for Photovoltaic Power Forecasting

Author

Listed:
  • Quanhui Qiu

    (Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Dejun Ning

    (Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Qiang Guo

    (HCIG AVIC Saihan Green Energy Technology Development Co., Ltd., Chengde 067400, China)

  • Jiang Wei

    (HCIG AVIC Saihan Green Energy Technology Development Co., Ltd., Chengde 067400, China)

  • Huichang Chen

    (HCIG AVIC Saihan Green Energy Technology Development Co., Ltd., Chengde 067400, China)

  • Lihui Sui

    (HCIG AVIC Saihan Green Energy Technology Development Co., Ltd., Chengde 067400, China)

  • Yi Liu

    (HCIG AVIC Saihan Green Energy Technology Development Co., Ltd., Chengde 067400, China)

  • Zibing Du

    (HCIG AVIC Saihan Green Energy Technology Development Co., Ltd., Chengde 067400, China)

  • Shipeng Liu

    (Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Photovoltaic power forecasting plays a crucial role in the integration of renewable energy into the power grid. However, existing methods suffer from issues such as cumulative multi-step prediction errors and the limitations of traditional evaluation metrics (e.g., MSE, MAE). To address these challenges, this study introduces DTCformer, a generative forecasting model based on Autoformer. The proposed model integrates a Temporal Convolution Feedforward Network module and a Variable Selection Embedding module, effectively capturing inter-variable dependencies and temporal periodicity. Furthermore, it incorporates the DILATE loss function, which significantly enhances both forecasting accuracy and robustness. Experimental results on publicly available datasets demonstrate that DTCformer surpasses mainstream models, improving overall performance metrics (DILATE values) by 5.0–42.3% in 24 h, 48 h, and 72 h forecasting tasks.

Suggested Citation

  • Quanhui Qiu & Dejun Ning & Qiang Guo & Jiang Wei & Huichang Chen & Lihui Sui & Yi Liu & Zibing Du & Shipeng Liu, 2025. "DTCformer: A Temporal Convolution-Enhanced Autoformer with DILATE Loss for Photovoltaic Power Forecasting," Energies, MDPI, vol. 18(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2450-:d:1652966
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    References listed on IDEAS

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    1. Chu Donatus Iweh & Samuel Gyamfi & Emmanuel Tanyi & Eric Effah-Donyina, 2021. "Distributed Generation and Renewable Energy Integration into the Grid: Prerequisites, Push Factors, Practical Options, Issues and Merits," Energies, MDPI, vol. 14(17), pages 1-34, August.
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    3. Mahmoud Dhimish & Pavlos I. Lazaridis, 2022. "Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems," Energies, MDPI, vol. 15(21), pages 1-16, November.
    4. Zagouras, Athanassios & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "On the role of lagged exogenous variables and spatio–temporal correlations in improving the accuracy of solar forecasting methods," Renewable Energy, Elsevier, vol. 78(C), pages 203-218.
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