In order to facilitate the shift towards sustainable practices and to support the transition to renewable energy, there is a requirement for faster and more accurate predictions of solar irradiance. Surface solar energy predictions are essential for the establishment of solar farms and the enhancement of energy grid management. This paper presents a novel approach to forecast surface solar irradiance up to 24 h in advance, utilizing various machine and deep learning architectures. Our proposed Machine Learning (ML) models include both point-based (1D) and grid-based (3D) solutions, offering a comprehensive exploration of different methodologies. Our forecasts leverage two days of input data to predict the next day of solar exposure at country scale. To assess the models’ performance, extensive testing is conducted across three distinct geographical areas of interest: Austria (where models were trained and validated), Switzerland and Italy (where we tested our models under a transfer learning regime), and sensitivity to the season is also discussed. The study incorporates comparisons with established benchmarks, including state-of-the-art numerical weather predictions, as well as fundamental predictors such as climatology and persistence. Our findings reveal that the ML-based methods clearly outperform traditional forecasting techniques, demonstrating high accuracy and reliability in predicting surface solar irradiance. This research not only contributes to the advancement of solar energy forecasting but also highlights the effectiveness of machine learning and deep learning models in being competitive to conventional methods for short-term solar irradiance predictions.

中文翻译：

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根据气象卫星数据对表面太阳辐照度进行机器学习预测


为了促进向可持续实践的转变并支持向可再生能源的过渡，需要更快、更准确地预测太阳辐照度。地面太阳能预测对于建立太阳能发电场和加强能源网格管理至关重要。本文提出了一种利用各种机器和深度学习架构提前 24 小时预测表面太阳辐照度的新颖方法。我们提出的机器学习 (ML) 模型包括基于点 (1D) 和基于网格 (3D) 的解决方案，提供对不同方法的全面探索。我们的预测利用两天的输入数据来预测国家范围内第二天的阳光照射。为了评估模型的性能，我们在三个不同的感兴趣的地理区域进行了广泛的测试：奥地利（模型在这些地区进行了训练和验证）、瑞士和意大利（我们在转移学习制度下测试了我们的模型）以及对季节的敏感性也进行了讨论。该研究结合了与既定基准的比较，包括最先进的数值天气预报，以及气候学和持久性等基本预测指标。我们的研究结果表明，基于机器学习的方法明显优于传统的预测技术，在预测表面太阳辐照度方面表现出高精度和可靠性。这项研究不仅有助于太阳能预测的进步，而且凸显了机器学习和深度学习模型在短期太阳辐照度预测方面与传统方法竞争的有效性。