The integration of renewable energy sources (RES) into modern power systems
has become increasingly important due to climate change and macroeconomic and
geopolitical instability. Among the RES, photovoltaic (PV) energy is rapidly
emerging as one of the world's most promising. However, its widespread adoption
poses challenges related to its inherently uncertain nature that can lead to
imbalances in the electrical system. Therefore, accurate forecasting of PV
production can help resolve these uncertainties and facilitate the integration
of PV into modern power systems. Currently, PV 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 in PV power forecasting.
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, 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 with an RMSE equal to 0.0460. 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.

该研究提出了一种新的基于自注意力转换器的混合方法，称为 MATNet，用于预测多元多步骤日前光伏发电量。MATNet 模型有效地结合了 AI 模型的优势与基于物理知识的模型，以提高光伏预测的准确性。