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

Quanhui Qiu, Dejun Ning (), Qiang Guo, Jiang Wei, Huichang Chen, Lihui Sui, Yi Liu, Zibing Du and Shipeng Liu
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Quanhui Qiu: Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
Dejun Ning: Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, 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

Energies, 2025, vol. 18, issue 10, 1-16

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.

Keywords: photovoltaic power forecasting; Autoformer; Transformer; TCN; Variable Selection Embedding; DILATE (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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