Accurate day-ahead forecasting of solar and wind generation remains a critical challenge for grid operators, since renewable output depends entirely on unpredictable atmospheric conditions rather than operator dispatch. Researchers have developed a comprehensive artificial intelligence framework that benchmarks renewable energy prediction under two distinct operational scenarios: strict blind forecasts relying only on historical patterns, and weather-informed forecasts incorporating day-ahead meteorological data.
The study employed NASA POWER satellite and reanalysis data to construct features representing photovoltaic and wind generation, battery state of charge, and multiple baseline predictors. Three machine learning models were evaluated—least-squares boosting, random forest regression, and neural networks—along with weighted ensemble combinations. Results under the blind scenario were disappointing: the best model achieved an R² value of 0.36 with root-mean-square error of 445.50 kW, indicating that historical patterns alone cannot reliably predict hourly renewable output 24 hours in advance.
The introduction of day-ahead weather predictors transformed performance dramatically. The LSBoost model reduced prediction error to 2.29 kW with R² = 0.99998, nearly perfect accuracy. However, researchers carefully frame this weather-informed result as an idealized upper bound rather than an immediately deployable operational tool. The synthetic generation targets were constructed directly from the same meteorological variables, and realized NASA POWER data replaced typical numerical weather prediction forecasts—conditions unlikely in real operations.
Despite these caveats, the dual-scenario strategy provides valuable insights. The stark performance gap explicitly demonstrates why renewable forecastability is fundamentally limited without meteorological data. The framework establishes practical benchmarks for energy planners and grid operators designing microgrid scheduling algorithms and battery management systems. Utilities can use these bounds to assess whether forecast accuracy improvements warrant investments in advanced weather prediction systems, and to set realistic expectations for what historical data alone can accomplish in renewable energy management.
