Title: Grouped Gaussian Processes for Solar Power Prediction,
Abstract: We consider multi-task regression models where the observations are assumed
to be a linear combination of several latent node functions and weight
functions, which are both drawn from Gaussian process priors. Driven by the
problem of developing scalable methods for forecasting distributed solar and
other renewable power generation, we propose coupled priors over groups of
(node or weight) processes to exploit spatial dependence between functions. We
estimate forecast models for solar power at multiple distributed sites and
ground wind speed at multiple proximate weather stations. Our results show that
our approach maintains or improves point-prediction accuracy relative to
competing solar benchmarks and improves over wind forecast benchmark models on
all measures. Our approach consistently dominates the equivalent model without
coupled priors, achieving faster gains in forecast accuracy. At the same time
our approach provides better quantification of predictive uncertainties.