Accurate forecasting of renewable generation is crucial to facilitate the integration of RES into the power system. Focusing on PV units, forecasting methods can be divided into two main categories: physics-based and data-based strategies, with AI-based models providing state-of-the-art performance. However, while these AI-based models can capture complex patterns and relationships in the data, they ignore the underlying physical prior knowledge of the phenomenon. Therefore, in this paper we propose MATNet, a novel self-attention transformer-based architecture for multivariate multi-step day-ahead PV power generation forecasting. It consists of a hybrid approach that combines the AI paradigm with the prior physical knowledge of PV power generation of physics-based methods. The model is fed with historical PV data and historical and forecast weather data through a multi-level joint fusion approach. The effectiveness of the proposed model is evaluated using the Ausgrid benchmark dataset with different regression performance metrics. The results show that our proposed architecture significantly outperforms the current state-of-the-art methods. These findings demonstrate the potential of MATNet in improving forecasting accuracy and suggest that it could be a promising solution to facilitate the integration of PV energy into the power grid.

翻译：可再生能源发电的精确预测对于促进其并网至关重要。针对光伏发电单元，预测方法主要分为基于物理机理和数据驱动两大类，其中基于人工智能的模型展现出最优性能。然而，这些人工智能模型虽能捕捉数据中的复杂模式和关联，却忽略了物理过程先验知识。为此，本文提出MATNet——一种基于自注意力机制的新型Transformer架构，用于多变量多步日前光伏发电功率预测。该模型采用混合策略，将人工智能范式与光伏发电基于物理方法的先验知识相结合。通过多层级联合融合方法，模型同时输入历史光伏发电数据、历史气象数据及预测气象数据。基于Ausgrid基准数据集并结合多种回归性能指标评估模型有效性，结果表明本文提出的架构显著优于现有最优方法。这些发现证实了MATNet在提升预测精度方面的潜力，表明其或将成为促进光伏并网的有效解决方案。