Deep learning has proven extremely successful both in classification and regression problems, especially when it is trained on very large datasets. In the space weather context, despite the unarguably large amount of data at our disposal, it remains an open question whether historical datasets contain enough information to build a predictive deep learning system. In this work, we use multi-wavelength solar images from SOHO (Solar and Heliospheric Observatory) as inputs to a deep convolutional neural network, to predict solar wind parameters observed at L1, 3 - 5 days ahead.