DAFT-E: Feature-Based Multivariate and Multi-Step-Ahead Wind Power Forecasting

Abstract

Wind energy is one of the most promising resources for the mitigation of greenhouse gas emissions that contribute to anthropogenic global warming. However, the large proliferation of wind power generators is causing several critical issues in power systems due to their variable power generated profiles. For this reason, a large number of learning techniques, e.g. integrating Vector Auto-Regressive and Neural Network-based models, were proposed in the literature for mitigating wind power uncertainty issues. Unfortunately, these methodologies show several limitations, e.g. the huge number of parameters and/or the heavy computational cost, which hinder their deployment in modern power system operation, where prompt and reliable wide-area wind power generation forecasts are requested for supporting time-critical decision making on several time horizons. To try addressing this issue, this paper proposes the Dynamic Adaptive Feature-based Temporal Ensemble (DAFT-E) forecasting approach, which relies on an extensive feature engineering, a fast feature selection step and an ensemble of computationally inexpensive models to reduce the computational complexity of the forecasting task, while still preserving predictive accuracy. The experimental results, which benchmark DAFT-E against multivariate (VAR and deep learning) alternatives on two real case studies, show that the proposed approach outperforms state-of-the-art and representation learning models according to several forecasting accuracy metrics.