Machine Learning for Sustainable Urban Energy Planning: A Comparative Model Analysis

Abstract

Accurate short-term forecasting of urban electricity demand is essential for operational planning and climate-resilient energy management. This study evaluates four forecasting models, namely, Prophet, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Networks (TCN), across 15 U.S. cities representing diverse climatic regimes. Model performance is assessed at 1, 6, 12, and 24 h horizons using MAE, RMSE, MAPE, and R2 within a unified, climate-aware evaluation framework. Results show that Prophet consistently outperforms deep learning models at longer horizons (12–24 h), achieving MAE reductions of approximately 70–90% relative to LSTM and GRU across all climatic clusters, while maintaining R2 values above 0.95 even in highly variable climates. At short horizons (1–6 h), LSTM and GRU perform competitively in climatically stable cities, reducing MAE by up to 15–25% compared with Prophet, but their accuracy deteriorates rapidly as forecast horizons increase. TCN exhibits intermediate performance, outperforming recurrent models in selected short-horizon cases but showing reduced robustness under high climate variability. Statistical testing indicates that model performance varies significantly across cities within climatically heterogeneous clusters (p < 0.05), highlighting the influence of climatic variability on forecasting reliability. Overall, the results demonstrate that model effectiveness is strongly context-dependent, providing quantitative guidance for climate-aware model selection in urban energy systems.