As the use of solar power increases, having accurate and timely forecasters will be essential for smooth grid operators. There are many proposed methods for forecasting solar irradiance / solar power production. However, many of these methods formulate the problem as a time-series, relying on near real-time access to observations at the location of interest to generate forecasts. This requires both access to a real-time stream of data and enough historical observations for these methods to be deployed. In this paper, we conduct a thorough analysis of effective ways to formulate the forecasting problem comparing classical machine learning approaches to state-of-the-art deep learning. Using data from 20 locations distributed throughout the UK and commercially available weather data, we show that it is possible to build systems that do not require access to this data. Leveraging weather observations and measurements from other locations we show it is possible to create models capable of accurately forecasting solar irradiance at new locations. We utilise compare both satellite and ground observations (e.g. temperature, pressure) of weather data. This could facilitate use planning and optimisation for both newly deployed solar farms and domestic installations from the moment they come online. Additionally, we show that training a single global model for multiple locations can produce a more robust model with more consistent and accurate results across locations.

翻译：随着太阳能利用的增加，精确且及时的预报系统对于电网的平稳运行至关重要。目前已有多种方法被提出用于预测太阳辐照度/太阳能发电量。然而，许多方法将其构建为时间序列问题，依赖于对目标位置近乎实时的观测数据来生成预报。这既需要实时数据流的接入，也需要足够的历史观测数据以部署这些方法。本文通过对比传统机器学习方法与最先进的深度学习方法，系统分析了构建太阳辐照度预测问题的有效途径。利用分布于英国各地的20个站点的数据以及商业可获取的气象数据，我们证明无需依赖目标位置观测数据即可构建有效的预测系统。通过利用其他站点的气象观测与测量数据，我们展示了构建模型以准确预测新位置太阳辐照度的可行性。我们对比了卫星与地面观测（如温度、气压）两类气象数据。这一成果有助于新投运的太阳能电站及家用光伏系统从并网伊始即可进行优化调度与规划。此外，研究还表明，针对多站点训练单一全局模型可生成更稳健的模型，且在不同站点间获得更一致、更精确的预测结果。