Short Time Wind Forecasting with Uncertainty

Forecasting the weather and especially the wind is important for a number of applications like wind farms or for maritime operations. Nowadays machine learning techniques are becoming more reliable and robust for forecasting due to the fact that a plethora of available datasets exist. However, forecasts for shorter time horizon less than two hour is not reliable due to the frequent wind fluctuations. Nevertheless, the need for algorithms that can have a small memory and cpu footprint is needed for hardware e.g. microcontrollers that are on board of vessels. In this manuscript a method for short time wind forecasting is proposed and scaled for a microcontroller. The method also computes prediction intervals with a certain probability. Our method was tested using real data recorded from a weather station on board of a ship conducting trips across the Aegean Sea (Greece).