Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes
This work addresses uncertainty-aware forecasting for powerplant management, representing an incremental improvement by applying existing scalable GP techniques to solar energy data.
The authors tackled short-term forecasting of solar photovoltaic energy production by developing a scalable Gaussian process model that provides predictions with error bars, achieving feasibility for large datasets and continuous data streams through state-space formulation and variational inference.
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.