  Rising penetration levels of (residential) photovoltaic (PV) power as
distributed energy resource pose a number of challenges to the electricity
infrastructure. High quality, general tools to provide accurate forecasts of
power production are urgently needed. In this article, we propose a supervised
deep learning model for end-to-end forecasting of PV power production. The
proposed model is based on two seminal concepts that led to significant
performance improvements of deep learning approaches in other sequence-related
fields, but not yet in the area of time series prediction: the sequence to
sequence architecture and attention mechanism as a context generator. The
proposed model leverages numerical weather predictions and high-resolution
historical measurements to forecast a binned probability distribution over the
prognostic time intervals, rather than the expected values of the prognostic
variable. This design offers significant performance improvements compared to
common baseline approaches, such as fully connected neural networks and
one-block long short-term memory architectures. Using normalized root mean
square error based forecast skill score as a performance indicator, the
proposed approach is compared to other models. The results show that the new
design performs at or above the current state of the art of PV power
forecasting.
