Cross-Zonal Short-Term Load Forecasting in Resource-Constrained Distribution Grids: A Multi-Model Benchmarking Study

Accurate short-term load forecasting is essential for modern power distribution, yet selecting optimal predictive architectures across diverse feeder profiles remains a key challenge for local grid operators. This paper presents a multi-feeder comparative evaluation of machine learning architectures using hourly load data (January 2024–April 2026) from six distribution feeders within two operational zones under the Bangladesh Rural Electrification Board. The evaluated models include statistical baselines (ARIMA, SARIMA), linear regression, tree ensembles (Random Forest, XGBoost, LightGBM), and recurrent architectures (LSTM, GRU). Feature engineering includes cyclical temporal encodings, weather parameters, and temporal lags. Experimental results demonstrate that tree-based ensembles consistently outperform both statistical baselines and recurrent neural networks across all feeders. Random Forest achieved the highest performance, with a Mean MAPE of 2.35%, and individual feeder errors reaching as low as 0.83% MAPE and 0.02 MW MAE. Meanwhile, LightGBM demonstrated superior operational deployment efficiency, delivering a highly comparable Mean MAPE of 2.56% with the lowest inference time of 0.55 s, and a minimal memory footprint of 1.2 MB. These findings highlight that feature-engineered tree ensembles provide an optimal balance of predictive performance, scalability, and computational efficiency for real-time distribution grid operations across neighboring utility zones.