One of the main sources of solar wind disturbances are coronal holes which can be identified in extreme ultra-violet (EUV) images of the Sun. Previous research has shown the connection between coronal holes and an increase of the solar wind speed at Earth. The time lag between the appearance of coronal holes on the visible side of the Sun and its effects on Earth is 2-5 days. In this study, a machine learning model predicting the solar wind speed originating from coronal holes is proposed. It is based on the analysis of solar EUV images. A segmentation algorithm is applied to the images in order to identify coronal holes and derive their characteristics (e.g. area, location). We also present a new method to calculate the geoeffective coronal hole area: Instead of specifying in advance a sector of the solar surface in which the area is measured and a lag time between area measurement and the arrival of the solar wind, the specification of this sector and the corresponding delay are formulated as a mathematical optimization problem and included in the machine learning model. This approach facilitates an improvement of the prediction accuracy and also prolongs the prediction horizon, as the solar wind speed can be predicted up to approximately 5 days in advance of the disturbance. Several machine learning model architectures are explored. We also study how the time evolution can be included in the model.