Photovoltaic (PV) power generation has emerged as one of the lead renewable energy sources. Yet, its production is characterized by high uncertainty, being dependent on weather conditions like solar irradiance and temperature. Predicting PV production, even in the 24 hour forecast, remains a challenge and leads energy providers to keep idle - often carbon emitting - plants. In this paper we introduce a Long-Term Recurrent Convolutional Network using Numerical Weather Predictions (NWP) to predict, in turn, PV production in the 24 hour and 48 hour forecast horizons. This network architecture fully leverages both temporal and spatial weather data, sampled over the whole geographical area of interest. We train our model on a NWP dataset from the National Oceanic and Atmospheric Administration (NOAA) to predict spatially aggregated PV production in Germany. We compare its performance to the persistence model and to state-of-the-art methods.

翻译：光伏发电已成为可再生能源的主导来源之一,然而,其生产具有高度不确定性,取决于太阳辐照和温度等天气条件。预测光伏发电仍然是一个挑战,即使在24小时的预报中,也仍然导致能源供应商保持闲置 -- -- 往往是碳排放 -- -- 的工厂。在本文中,我们引入了一个长期革命网络,利用数字天气预报(NWP)来预测24小时和48小时预报地平线的光伏发电。这个网络结构充分利用了时间和空间气象数据,这些数据是在整个感兴趣的地理区域取样的。我们用国家海洋和大气管理局(NOAA)的NWP数据集培训我们的模型,以预测德国的光电发电空间集成。我们将其性能与持久性模型和最新技术方法进行比较。