Wind Power Forecasting Architectures in Complex Terrain and Coastal Environments: A Systematic Review and Meta-Analysis of Comparative Performance

Wind forecasting in complex coastal terrain, such as the Colombian Caribbean, is hindered by air–sea heat fluxes and nonlinear atmospheric dynamics. This pre-registered systematic review and meta-analysis tests whether advanced architectures (artificial intelligence, hybrid, ensemble) outperform classical ones (physical, statistical). Scopus and Web of Science (last query: 22 September 2026) yielded 100 studies with extractable effect sizes; 76 compared a proposed model with an explicit comparator (primary analysis). Hedges’ g (negative values favor the proposed model) was pooled with REML random effects and Knapp–Hartung inference. The pooled effect was g=−1.07 (95% CI [−1.38,−0.75]; 95% prediction interval [−2.77,0.64]; I2=69.5%), robust to the estimator and to influential studies, although 59 comparisons were against a baseline of the same family. The estimates favored advanced over classical architectures, but not significantly (β=−0.30, 95% CI [−0.99,0.38]); only 10 studies compared them directly, and 9 favored the advanced model (g=−0.78, 95% CI [−1.93,0.36]). Only AI (g=−1.08, k=48) and physical (g=−0.93, k=19) models had intervals excluding zero. Egger and Begg tests indicated funnel-plot asymmetry, but the Pustejovsky–Rodgers test, robust to the dependence of the standard error on the effect, did not. Proposed models outperform their baselines; the superiority of any architectural class is not established.