Downscaling and Predicting Downward Shortwave Radiation

Music City Center 

Accurately predicting Downward Shortwave Radiation (DSWR) is important for renewable energy, agriculture, and environmental studies. Global datasets provide DSWR estimates at coarse resolutions but often lack the localized precision required for tasks like energy system planning. This study introduces NN-XGBoost, a novel method that combines nearest-neighbor smoothing with the predictive power of eXtreme Gradient Boosting (XGBoost) to enhance accuracy in downscaling and predicting DSWR.

The proposed model leverages global DSWR data from Open-Meteo and local observations from Ambient Weather. Two prediction strategies are examined: (1) using a single local variable and (2) using multiple local variables. Results show that NN-XGBoost consistently outperforms both XGBoost and ARIMAX, achieving lower error (RMSE) and higher accuracy (\(R^2\)). This method provides a practical and scalable approach to improving DSWR forecasting and has significant applications in renewable energy planning, environmental monitoring, and agricultural decision-making.

Downward Shortwave Radiation

Nearest-Neighbor XGBoost

Forecast 

Section on Statistical Learning and Data Science