In the probabilistic energy forecasting literature, emphasis is mainly placed
on deriving marginal predictive densities for which each random variable is
dealt with individually. Such marginals description is sufficient for power
systems related operational problems if and only if optimal decisions are to be
made for each lead-time and each location independently of each other. However,
many of these operational processes are temporally and spatially coupled, while
uncertainty in photovoltaic (PV) generation is strongly dependent in time and
in space. This issue is addressed here by analysing and capturing
spatio-temporal dependencies in PV generation. Multivariate predictive
distributions are modelled and space-time trajectories describing the potential
evolution of forecast errors through successive lead-times and locations are
generated. Discrimination ability of the relevant scoring rules on performance
assessment of space-time trajectories of PV generation is also studied.
Finally, the advantage of taking into account space-time correlations over
probabilistic and point forecasts is investigated. The empirical investigation
is based on the solar PV dataset of the Global Energy Forecasting Competition
(GEFCom) 2014.Comment: 33 pages, 11 Figure