A Unified Geoai Hybrid Framework For African Windscapes: Integrating Evolutionary Swarm, Fuzzy-Neuro Computing, And Probabilistic Spatio-Temporal Wind Forecasting For Sustainable Regional Energy Planning

Abstract

Africa is expected to contribute more than fifty gigawatts of installed wind-generation capacity by 2035, but the
practical utilization of this operational capacity on national and regional grids is limited/ hampered by the
persistent occlusion of point-forecast methodologies ill-fit to the continent's diverse orographic, monsoonal, &
continental/coastal wind regimes. In this work, we propose an integrated hybrid GeoAI of a multi-paradigm
approach that comprehensively unifies four popular computational intelligence paradigms (evolutionary
algorithms; particle swarm optimization; adaptive neuro-fuzzy inference; and convolutional-recurrent deep
learning) into one five-layer architecture for probabilistic spatiotemporal wind speed forecasting at a pan-African
scale across twelve diverse sites in seven countries including South Africa, Egypt, Morocco, Kenya, Ethiopia,
Mauritania and Senegal. The framework utilizes 736,416 hourly observations over January 2018 to December
2024 obtained from ERA5 reanalysis, MERRA-2, NASA POWER, and Weather Research and Forecasting (WRF)
mesoscale downscaling with SRTM digital elevation and land-cover layers at a grid resolution of 0.25° × 0.25°.
The output layer renders probabilistic forecasts at five quantiles (τ =0.05, 0.25, 0.50, 0.75, and 0.95) across five
forecast horizons (1 h,6 h,24 h,72 h and 168h). Bootstraped with a Lyapunov-stability theorem, the hybrid
framework converges in 75 iterations to a best-so-far RMes of 0.51 m/s (45 %lower than relative dynamical
predictor, CNN-LSTM baseline = 0.92 m/s). A continuous ranked probability score (CRPS) of 0.31 m/s and a
near uniform PIT histogram confirm the well-calibrated probabilistic forecasts. As a conclusion, the
computational complexity is (N · T · log T ·d) where the number of sites, the time horizon (size of your input
sequences), the feature dimension. The deterministic World Bank is a powerful uplift in capacity-factor estimation,
with implementation of the probabilistic forecasts producing an effective uplift of 3.3–4.3 percentage points across
the twelve sites tested against this baseline ∼320 GWh/year extra useable generation across the surveyed African
wind portfolio. The framework delineates a methodologically firm, computationally feasible and operationally
applicable GeoAI pathway to climate resilient regional energy planning in Sub-Saharan and North Africa.