•Novel fully-convolutional models are proposed to improve multi-site wind forecasting.•The fully-convolutional models specialize in processing zonal and meridional velocities.•Specialized processing of zonal and meridional velocities improves wind forecasting.•The approach is suitable to predict phenomena composed by multiple factors. The increasing presence of intermittent renewables in modern power systems motivates the development of methods for renewables forecasting. More accurate forecasts may implicate less operational costs for power systems. In this context, this paper proposes a family of architectures based on fully convolutional neural networks for wind speed prediction, the ComPonentNet (CPNet) family. The CPNet produces multi-site spatio-temporal forecasting for phenomena which may be decomposed into multiple components (e.g., wind, which may be decomposed into u- and v-wind). The CPNet family includes three architectures - the core CPNet, the fully-fused CPNet and the bottom-fused CPNet. Each architecture processes the components of the phenomenon in a different manner - in separate branches of convolutional operations, in the same branch, or mixing separate and joint branches. This paper investigates the performance of each CPNet architecture in forecasting multi-site spatio-temporal wind speed. Moreover, the CPNet framework is compared against the U-Net architecture. The results indicate that the proposed framework is promising, and that splitting the processing of wind components may be beneficial to spatio-temporal forecasting, with results that outperform the U-Net.