Forecasting Urban Energy for Net-Zero Smart Cities: A Hybrid Attention-based Approach
Date:
In this talk, I presented two hybrid deep learning architectures for net-zero smart city energy forecasting, integrating CNNs for short-term feature extraction, LSTMs for long-range temporal modeling, and an Attention mechanism for dynamic weighting of time-step importance. Evaluated on the ENTSO-E dataset, the proposed models outperformed all baselines, with CNN-LSTM-Att.v2 achieving the best single-step results (RMSE 106.96 MW, MAPE 1.07%) and CNN-LSTM-Att.v1 delivering the strongest multi-step day-ahead performance (RMSE 438.11 MW, MAPE 4.18%). These findings demonstrate the effectiveness of attention-driven spatiotemporal modeling for accurate and scalable energy forecasting in sustainable smart cities. The talk can be found here.