Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning

Integrating enormous quantities of wind energy into the electrical system requires precise planning and forecasting. This paper presents a novel framework for wind power forecasting, which establishes a new standard for accuracy and reliability. It uses advanced deep neural networks and a stacking ensemble mechanism to make probabilistic forecasts. These forecasts address wind speed volatility and variability, which continue to be key issues in producing consistently accurate wind power forecasts despite advances in deep learning. Base learners generate forecasts that include lower and upper bounds, as well as median prediction intervals. The Huber regressor, also known as the Meta-learner, combines projections from many models to reduce susceptibility to extreme values. Expanding window cross-validation is used in performance evaluation, with a focus on Mean Absolute Error, Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error, Prediction Interval Coverage Probability, and Average Interval Width for one, two, and three step ahead predictions. The model’s stability is measured by the standard deviation of MSE throughout each validation window. Additionally, the Diebold–Mariano test is used to validate the Meta-learner’s predictive accuracy in contrast to other advanced models and the seasonal naïve benchmark. This study found that the meta model outperformed all other models in eight of the nine forecasts tested, indicating its effectiveness in medium-range forecasting. In Germany, it exceeded Temporal Fusion Transformer by two and three steps ahead, with 22% and 34% improvements, respectively. Indeed, this study not only provides a reliable prediction tool, but also lays foundations for efficient and effective energy management and policy planning in the renewable energy industry.