Due to the rise in the use of renewable energies as an alternative to traditional ones, and especially solar energy, there is increasing interest in studying how to address photovoltaic forecasting in the face of the challenge of variability in photovoltaic energy production, using different methodologies. This work develops a hybrid approach for short and long-term forecasting based on two studies with the same purpose. A multimodal approach that combines images of the sky and photovoltaic energy history with meteorological data is proposed. The main goal is to improve the accuracy of ramp event prediction, increase the robustness of forecasts in cloudy conditions, and extend capabilities beyond nowcasting, to support more efficient operation of the power grid and better management of solar variability. Deep neural models are used for both nowcasting and forecasting solutions, incorporating individual and multiple meteorological variables, as well as an analytical solar position. The results demonstrate that the inclusion of meteorological data, particularly the surface long-wave, radiation downwards, and the combination of wind and solar position, significantly improves current predictions in both nowcasting and forecasting tasks, especially on cloudy days. This study highlights the importance of integrating diverse data sources to improve the reliability and interpretability of solar energy prediction models.

翻译：随着可再生能源（尤其是太阳能）作为传统能源替代品的使用日益增加，人们越来越关注如何应对光伏能源生产波动性带来的挑战，并采用不同方法研究光伏功率预测问题。本研究基于两项目标相同的研究，开发了一种用于短期和长期预测的混合方法。提出了一种多模态方法，将天空图像和光伏发电历史数据与气象数据相结合。其主要目标是提高功率陡升事件预测的准确性，增强多云条件下预测的鲁棒性，并扩展临近预报之外的能力，以支持电网更高效运行和太阳能波动性的更好管理。研究采用深度神经网络模型实现临近预报和预测解决方案，整合了单个及多个气象变量以及解析计算的太阳位置。结果表明，加入气象数据（特别是地表长波辐射、向下辐射以及风与太阳位置的组合）能显著改善当前在临近预报和预测任务中的预测性能，尤其在多云天气条件下。本研究强调了整合多样化数据源对于提高太阳能预测模型可靠性和可解释性的重要性。