  The energy market relies on forecasting capabilities of both demand and power
generation that need to be kept in dynamic balance. Today, when it comes to
renewable energy generation, such decisions are increasingly made in a
liberalized electricity market environment, where future power generation must
be offered through contracts and auction mechanisms, hence based on forecasts.
The increased share of highly intermittent power generation from renewable
energy sources increases the uncertainty about the expected future power
generation. Point forecast does not account for such uncertainties. To account
for these uncertainties, it is possible to make probabilistic forecasts. This
work first presents important concepts and approaches concerning probabilistic
forecasts with deep learning. Then, deep learning models are used to make
probabilistic forecasts of day-ahead power generation from a wind power plant
located in Northern Norway. The performance in terms of obtained quality of the
prediction intervals is compared for different deep learning models and sets of
covariates. The findings show that the accuracy of the predictions improves
when historical data on measured weather and numerical weather predictions
(NWPs) were included as exogenous variables. This allows the model to
auto-correct systematic biases in the NWPs using the historical measurement
data. Using only NWPs, or only measured weather as exogenous variables, worse
prediction performances were obtained.
