Title: A Comprehensive Assessment of Existing Frameworks in Wind Power Prediction

Abstract:

Accurate interval forecasting of wind speeds is critical for the seamless incorporation of wind energy into electrical grids, primarily because it addresses the unpredictable nature of wind resources. This paper provides a thorough systematic review of hybrid methodologies utilized in wind generation interval forecasting. The research investigates the synergistic integration of statistical techniques, deep learning algorithms, and modal decomposition strategies. To facilitate the selection of relevant literature, Latent Dirichlet Allocation (LDA) was employed for topic modeling, which helped uncover underlying patterns and emerging research trajectories.

The analysis reveals that combining hybrid models with decomposition methods—specifically Ensemble Empirical Mode Decomposition (EEMD) and Variational Mode Decomposition (VMD)—significantly boosts both the reliability and accuracy of predictions. These techniques achieve this by tightening prediction intervals while maintaining adequate coverage rates. In terms of constructing these intervals, the majority of reviewed studies utilize a dual-model framework, where the lower and upper limits are predicted independently.

Data preprocessing typically involves decomposing inputs via methods such as EMD, EEMD, or VMD to isolate frequency-specific components. These extracted components are then fed into models like Extreme Learning Machines (ELM) or Long Short-Term Memory (LSTM) networks, which are trained separately for each bound. This strategy enables the precise modeling of uncertainty, thereby enhancing both flexibility and accuracy. The quality of these intervals is generally assessed using metrics that weigh the trade-off between interval width and coverage probability.

Despite these advancements, the review identifies several ongoing challenges, such as the absence of standardized evaluation metrics, high computational demands, and a scarcity of validation in real-world scenarios. Ultimately, the study underscores the importance of interval forecasting for wind energy management and provides valuable perspectives for enhancing model resilience and supporting better decision-making processes.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC