Feature Engineering and Forecasting via Integration of Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks: Renewable Energy Case Studies

This research introduces a framework for forecasting, reconstruction and feature engineering of multivariate processes. We integrate derivative-free optimization with ensemble of sequence-to-sequence networks. We design a new resampling technique called additive which along with Bootstrap aggregating (bagging) resampling are applied to initialize the ensemble structure. We explore the proposed framework performance on three renewable energy sources wind, solar and ocean wave. We conduct several short- to long-term forecasts showing the superiority of the proposed method compare to numerous machine learning techniques. The findings indicate that the introduced method performs reasonably better 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. We investigate the effects of different environmental and marine factors on the wind speed and ocean output power respectively and report the selected features. Moreover, we explore the online forecasting setting and illustrate that the model exceeds alternatives through different measurement errors.