  An accurate solar wind speed model is important for space weather
predictions, catastrophic event warnings, and other issues concerning solar
wind - magnetosphere interaction. In this work, we construct a model based on
convolutional neural network (CNN) and Potential Field Source Surface (PFSS)
magnetograms, considering a solar wind source surface of $R_{\rm
SS}=2.5R_\odot$, aiming to predict the solar wind speed at the Lagrange 1 (L1)
point of the Sun-Earth system. The input of our model consists of four
Potential Field Source Surface (PFSS) magnetograms at $R_{\rm SS}$, which are
7, 6, 5, and 4 days before the target epoch. Reduced magnetograms are used to
promote the model's efficiency. We use the Global Oscillation Network Group
(GONG) photospheric magnetograms and the potential field extrapolation model to
generate PFSS magnetograms at the source surface. The model provides
predictions of the continuous test dataset with an averaged correlation
coefficient (CC) of 0.52 and a root mean square error (RMSE) of 80.8 km/s in an
eight-fold validation training scheme with the time resolution of the data as
small as one hour. The model also has the potential to forecast high speed
streams of the solar wind, which can be quantified with a general threat score
of 0.39.
