Short-term forecasting of solar photovoltaic energy (PV) production is important for powerplant management. Ideally these forecasts are equipped with error bars, so that downstream decisions can account for uncertainty. To produce predictions with error bars in this setting, we consider Gaussian processes (GPs) for modelling and predicting solar photovoltaic energy production in the UK. A standard application of GP regression on the PV timeseries data is infeasible due to the large data size and non-Gaussianity of PV readings. However, this is made possible by leveraging recent advances in scalable GP inference, in particular, by using the state-space form of GPs, combined with modern variational inference techniques. The resulting model is not only scalable to large datasets but can also handle continuous data streams via Kalman filtering.

翻译：太阳能光伏发电（PV）的短期预测对电厂管理至关重要。理想情况下，这些预测应配备误差区间，以便下游决策能够充分考虑不确定性。为在此场景下生成带误差区间的预测，我们采用高斯过程（GPs）对英国太阳能光伏发电量进行建模与预测。由于光伏时间序列数据规模庞大且存在非高斯性，标准GP回归方法难以直接应用。然而，通过利用可扩展GP推理的最新进展，特别是结合状态空间形式的GP与现代变分推理技术，该问题得以解决。所得模型不仅能扩展至大规模数据集，还能通过卡尔曼滤波处理持续数据流。