  This article implements a Convolutional Neural Network (CNN)-based deep
learning model for solar-wind prediction. Images from the Atmospheric Imaging
Assembly (AIA) at 193\.A wavelength are used for training. Solar-wind speed is
taken from the Advanced Composition Explorer (ACE) located at the Lagrangian L1
point. The proposed CNN architecture is designed from scratch for training with
four years' data. The solar-wind has been ballistically traced back to the Sun
assuming a constant speed during propagation, to obtain the corresponding
coronal intensity data from AIA images. This forecasting scheme can predict the
solar-wind speed well with a RMSE of 76.3 km\s and an overall correlation
coefficient of 0.57 for the year 2018, while significantly outperforming
benchmark models. The threat score for the model is around 0.46 in identifying
the HSEs with zero false alarms.
