  Two-stage ensemble-based forecasting methods have been studied extensively in
the wind power forecasting field. However, deep learning-based wind power
forecasting studies have not investigated two aspects. In the first stage,
different learning structures considering multiple inputs and multiple outputs
have not been discussed. In the second stage, the model extrapolation issue has
not been investigated. Therefore, we develop four deep neural networks for the
first stage to learn data features considering the input-and-output structure.
We then explore the model extrapolation issue in the second stage using
different modeling methods. Considering the overfitting issue, we propose a new
moving window-based algorithm using a validation set in the first stage to
update the training data in both stages with two different moving window
processes.Experiments were conducted at three wind farms, and the results
demonstrate that the model with single input multiple output structure obtains
better forecasting accuracy compared to existing models. In addition, the ridge
regression method results in a better ensemble model that can further improve
forecasting accuracy compared to existing machine learning methods. Finally,
the proposed two-stage forecasting algorithm can generate more accurate and
stable results than existing algorithms.
