Qiu, Quanhui; Ning, Dejun; Guo, Qiang; Wei, Jiang; Chen, Huichang; Sui, Lihui; Liu, Yi; Du, Zibing; Liu, Shipeng

ENERGIES

2025, 10,

Qiu, Quanhui; Ning, Dejun; Guo, Qiang; Wei, Jiang; Chen, Huichang; Sui, Lihui; Liu, Yi; Du, Zibing; Liu, Shipeng

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.