  The supply and demand of energy is influenced by meteorological conditions.
The relevance of accurate weather forecasts increases as the demand for
renewable energy sources increases. The energy providers and policy makers
require weather information to make informed choices and establish optimal
plans according to the operational objectives. Due to the recent development of
deep learning techniques applied to satellite imagery, weather forecasting that
uses remote sensing data has also been the subject of major progress. The
present paper investigates multiple steps ahead frame prediction for coastal
sea elements in the Netherlands using U-Net based architectures. Hourly data
from the Copernicus observation programme spanned over a period of 2 years has
been used to train the models and make the forecasting, including seasonal
predictions. We propose a variation of the U-Net architecture and further
extend this novel model using residual connections, parallel convolutions and
asymmetric convolutions in order to introduce three additional architectures.
In particular, we show that the architecture equipped with parallel and
asymmetric convolutions as well as skip connections outperforms the other three
discussed models.
