  This study introduces a framework for the forecasting, reconstruction and
feature engineering of multivariate processes along with its renewable energy
applications. We integrate derivative-free optimization with an ensemble of
sequence-to-sequence networks and design a new resampling technique called
additive resampling, which, along with Bootstrap aggregating (bagging)
resampling, are applied to initialize the ensemble structure. Moreover, we
explore the proposed framework performance on three renewable energy
sources---wind, solar and ocean wave---and conduct several short- to long-term
forecasts showing the superiority of the proposed method compared to numerous
machine learning techniques. The findings indicate that the introduced method
performs more accurately when the forecasting horizon becomes longer. In
addition, we modify the framework for automated feature selection. The model
represents a clear interpretation of the selected features. Furthermore, we
investigate the effects of different environmental and marine factors on the
wind speed and ocean output power, respectively, and report the selected
features. Finally, we explore the online forecasting setting and illustrate
that the model outperforms alternatives through different measurement errors.
