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This study introduces a framework for the forecasting, reconstruction andfeature engineering of multivariate processes along with its renewable energyapplications. We integrate derivative-free optimization with an ensemble ofsequence-to-sequence networks and design a new resampling technique calledadditive resampling, which, along with Bootstrap aggregating (bagging)resampling, are applied to initialize the ensemble structure. Moreover, weexplore the proposed framework performance on three renewable energysources---wind, solar and ocean wave---and conduct several short- to long-termforecasts showing the superiority of the proposed method compared to numerousmachine learning techniques. The findings indicate that the introduced methodperforms more accurately when the forecasting horizon becomes longer. Inaddition, we modify the framework for automated feature selection. The modelrepresents a clear interpretation of the selected features. Furthermore, weinvestigate the effects of different environmental and marine factors on thewind speed and ocean output power, respectively, and report the selectedfeatures. Finally, we explore the online forecasting setting and illustratethat the model outperforms alternatives through different measurement errors.

energy

Feature Engineering and Forecasting via Derivative-free Optimization and  Ensemble of Sequence-to-sequence Networks with Applications in Renewable  Energy