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The supply and demand of energy is influenced by meteorological conditions.The relevance of accurate weather forecasts increases as the demand forrenewable energy sources increases. The energy providers and policy makersrequire weather information to make informed choices and establish optimalplans according to the operational objectives. Due to the recent development ofdeep learning techniques applied to satellite imagery, weather forecasting thatuses remote sensing data has also been the subject of major progress. Thepresent paper investigates multiple steps ahead frame prediction for coastalsea elements in the Netherlands using U-Net based architectures. Hourly datafrom the Copernicus observation programme spanned over a period of 2 years hasbeen used to train the models and make the forecasting, including seasonalpredictions. We propose a variation of the U-Net architecture and furtherextend this novel model using residual connections, parallel convolutions andasymmetric convolutions in order to introduce three additional architectures.In particular, we show that the architecture equipped with parallel andasymmetric convolutions as well as skip connections outperforms the other threediscussed models.

Deep coastal sea elements forecasting using U-Net based models