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<p>Accurate photovoltaic (PV) power forecasting is essential for power system scheduling and security, yet it is challenged by the high uncertainty of solar generation due to weather, season, and location. Although Transformer-based models have advanced prediction capabilities, they still suffer from key limitations: their point-wise self-attention underuses local context, they capture short-term dependencies poorly, and their numerous hyperparameters require experience-heavy manual tuning, complicating optimization. To overcome these issues, this paper proposes a novel Transformer-based model named MSLP-Gruformer for PV power forecasting. Built upon the iTransformer framework, the model incorporates a Multi-scale Local Perceptual Attention Mechanism (MSLP) to enhance the extraction of local features and trend variations in time-series data. Furthermore, by integrating a Gated Recurrent Unit (GRU) module, the model strengthens its ability to jointly capture both long- and short-term dependencies. Hyperparameter optimization is automated using the Blood-Sucking Leech Optimizer (BSLO), which reduces computational cost and improves tuning efficiency. The performance of MSLP-Gruformer is evaluated using real-world data from four PV stations located in China and Australia. Forecasts are generated for four time horizons: 1&#xa0;h, 4&#xa0;h, 12&#xa0;h, and 24&#xa0;h, and compared with the baseline iTransformer model. Compared with GRU, TCN, Transformer-GRU, Transformer-TCN, Crossformer and iTransformer, results show that MSLP-Gruformer achieves consistently lower prediction errors and higher goodness-of-fit across all forecasting horizons and station capacities, demonstrating its potential to support daily power system scheduling and enhance operational security.</p>

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MSLP-Gruformer: a hybrid model based on iTransformer for day-ahead photovoltaic power forecasting

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Dongcheng Mo,
  • Yue Gao,
  • Zhong Wu,
  • Yonggang Wang,
  • Shengzhou Feng

摘要

Accurate photovoltaic (PV) power forecasting is essential for power system scheduling and security, yet it is challenged by the high uncertainty of solar generation due to weather, season, and location. Although Transformer-based models have advanced prediction capabilities, they still suffer from key limitations: their point-wise self-attention underuses local context, they capture short-term dependencies poorly, and their numerous hyperparameters require experience-heavy manual tuning, complicating optimization. To overcome these issues, this paper proposes a novel Transformer-based model named MSLP-Gruformer for PV power forecasting. Built upon the iTransformer framework, the model incorporates a Multi-scale Local Perceptual Attention Mechanism (MSLP) to enhance the extraction of local features and trend variations in time-series data. Furthermore, by integrating a Gated Recurrent Unit (GRU) module, the model strengthens its ability to jointly capture both long- and short-term dependencies. Hyperparameter optimization is automated using the Blood-Sucking Leech Optimizer (BSLO), which reduces computational cost and improves tuning efficiency. The performance of MSLP-Gruformer is evaluated using real-world data from four PV stations located in China and Australia. Forecasts are generated for four time horizons: 1 h, 4 h, 12 h, and 24 h, and compared with the baseline iTransformer model. Compared with GRU, TCN, Transformer-GRU, Transformer-TCN, Crossformer and iTransformer, results show that MSLP-Gruformer achieves consistently lower prediction errors and higher goodness-of-fit across all forecasting horizons and station capacities, demonstrating its potential to support daily power system scheduling and enhance operational security.