Two-stage short-term wind power forecasting algorithm using different feature-learning models

TL;DR

This paper proposes a novel two-stage deep learning-based wind power forecasting algorithm that considers different input-output structures and addresses model extrapolation, resulting in improved accuracy and stability.

Contribution

It introduces four deep neural networks with varied structures for the first stage and explores model extrapolation in the second, along with a moving window update method to enhance forecasting.

Findings

Single input multiple output structure outperforms existing models.

Ridge regression ensemble improves forecasting accuracy.

Proposed method yields more accurate and stable forecasts.

Abstract

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.