SunCast：地球同步卫星资料的太阳辐照度临近预报
SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data
摘要：
当云层覆盖光伏（PV）面板时，面板产生的电量会迅速波动。因此，为了在电网上保持足够的能量以满足需求，公用事业公司依赖于通常来自化石燃料的备用电源，因此污染环境。准确的短期光伏发电预测使运营商能够最大限度地提高从光伏电池板获得的电量，并安全地减少化石燃料源所需的储备能源。虽然一些研究已经开发了机器学习模型来预测特定光伏发电设施的太阳辐照度，但在全球范围内对短期太阳辐照度进行建模的工作很少。此外，已经开发的模型是专有的，并且具有非公开可用的架构，或者依赖于计算要求高的数值天气预报（NWP）模型。在这里，我们提出了一个卷积长短期记忆网络模型，将太阳临近预报作为下一帧预测问题，比NWP模型更有效，并且具有简单，可重复的架构。我们的模型可以在没有GPU的单台机器上在60秒内预测整个北美长达3小时的太阳辐照度，并且在2个月的数据评估时具有120 W/m2的RMSE。
英文摘要：
When cloud layers cover photovoltaic (PV) panels, the amount of power the panels produce fluctuates rapidly. Therefore, to maintain enough energy on a power grid to match demand, utilities companies rely on reserve power sources that typically come from fossil fuels and therefore pollute the environment. Accurate short-term PV power prediction enables operators to maximize the amount of power obtained from PV panels and safely reduce the reserve energy needed from fossil fuel sources. While several studies have developed machine learning models to predict solar irradiance at specific PV generation facilities, little work has been done to model short-term solar irradiance on a global scale. Furthermore, models that have been developed are proprietary and have architectures that are not publicly available or rely on computationally demanding Numerical Weather Prediction (NWP) models. Here, we propose a Convolutional Long Short-Term Memory Network model that treats solar nowcasting as a next frame prediction problem, is more efficient than NWP models and has a straightforward, reproducible architecture. Our models can predict solar irradiance for entire North America for up to 3 hours in under 60 seconds on a single machine without a GPU and has a RMSE of 120 W/m2 when evaluated on 2 months of data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
