Wind Power Forecasting Architectures in Complex Terrain and Coastal Environments: A Systematic Review and Meta-Analysis of Comparative Performance
Abstract
Accurate wind resource estimation in complex environments, such as the Colombian Caribbean, is challenging due to air–sea heat fluxes and nonlinear atmospheric dynamics. This systematic review and meta-analysis synthesized empirical evidence from 49 studies to evaluate whether advanced forecasting architectures (artificial intelligence, ensemble, and hybrid models) systematically outperform classical physical models. The pooled random-effects estimate indicated an overall reduction in reported forecasting error ($g = -1.6874$), though heterogeneity was extreme ($I^2 = 99.99\%$). Categorical meta-regression revealed no statistically significant advantage for advanced architectures over physical models ($\beta_1 = 0.2558$, $p = 0.9339$). Mixed-effects meta-regression identified Sample Size ($N > 10{,}000$) as the primary significant moderator, with the final model explaining 40.47% of between-study variability ($R^2_{\text{analog}} = 40.47\%$, reducing residual variance from $\tau^2 = 2.5102$ to $\tau^2 = 1.4943$). Metric-specific contrasts indicated that physical and AI architectures are statistically indistinguishable, whereas hybrid models yielded lower pooled effects. classical physical models remain a reliable, stable reference framework, whereas AI and hybrid approaches demonstrate advantages only in specific, non-generalizable settings. These findings challenge the assumption of universal superiority for AI-based wind forecasting in complex coastal domains.
