The ongoing large scale adoption of wind power increases the associated risks related to the variability. An essential way to mitigate these risks for a utility company is to forecast production accurately. This study aims to create insight into the potential of deep learning models for both forecast quality and value on the ultra-short-term wind power forecasting (UST-WPF) horizon. The status quo at Eneco, a Dutch utility company, for UST-WPF is a numerical weather prediction (NWP) based model with a rudimentary ultra-short-term (UST) correction with real-time power data. The methodology followed during this thesis was the development of four UST-WPF models for Princess Amalia Wind Farm (PAWP) with a 16 programme time unit (PTU) forecast horizon and a forecast frequency of 1 PTU. Both model 1 and model 2 only use real-time data and are based on a multilayer perceptron (MLP) and a long short-term memory (LSTM) architecture, respectively. The other two proposed models are a multivariate combination of these two respective models with the Eneco model. The accuracy of the four proposed models was compared to two benchmark models: a Persistence and the Eneco model. Additionally, a novel framework was designed to evaluate the forecast value relative to the Eneco model on a variable forecast horizon. This study indicates that the proposed deep learning models can contribute both in quality and value up to 9 PTUs ahead.