As the use of solar power increases, having accurate and timely forecasts 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 propose the use of Global methods to train our models in a generalised way, enabling them to generate forecasts for unseen locations. We apply this approach to both classical ML and state of the art methods. Using data from 20 locations distributed throughout the UK and widely available weather data, we show that it is possible to build systems that do not require access to this data. We utilise and compare both satellite and ground observations (e.g. temperature, pressure) of weather 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. 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，准确及时的预测对于电网平稳运行至关重要。目前已有多种预测太阳辐照度/太阳能发电量的方法，但许多方法将问题建模为时间序列，依赖对目标地点观测数据的近实时访问来生成预测。这既需要实时数据流支持，也需要充足的历史观测数据才能部署。本文提出采用全局方法以广义方式训练模型，使其能够对未知地点进行预测。我们将该方法应用于经典机器学习与最新技术。通过使用英国境内20个地点的数据及广泛可用的气象数据，研究表明可以构建无需访问实时数据的预测系统。我们利用并比较了卫星与地面观测（如温度、气压）两类气象数据。借助其他地点的气象观测与测量数据，我们证明能够创建准确预测新地点太阳辐照度的模型。这有助于新投产的太阳能电站和家用装置从并网起即可进行规划与优化。此外，研究表明训练单一全局模型处理多个地点，可产生更稳健的模型，在不同地点间获得更一致且准确的预测结果。