Enhancing Forecast Accuracy through Adaptive Deep Learning: A Comparative Analysis of Hybrid Neural Models

Accurate short-term electricity demand prediction is the key to the successful operation of the power system. In this paper, a hybrid deep learning framework is suggested to be used to adapt neural networks to non-stationary load patterns caused by seasonality, holidays, and extreme weather conditions. This proposed framework combines residual learning with day-to-day adaptive updates such that the forecasting performance is stabilized with time. The experiments are performed with the hourly load data of PGCB (Power Grid Company of Bangladesh) and population-weighted weather and calendar characteristics for July 2021 to June 2025. There are five hybrid architectures, including GRU-TCN-Attention, CNN-LSTM, CNN-GRU, BiLSTM-Attention, and LSTM-Transformer. The rolling report of July 2025 has shown that the adaptive learning is always better in accuracy and robustness than all the models. The suggested GRU-TCN-Attention model gives a MAPE (Mean Absolute Percentage Error) of 4.43% and an RMSE (Root Mean Square Error) of 709 MW and makes fewer errors than the non-volatile baseline by 47% and generates 32% fewer unstable predictions. Our results validate the fact that adaptive hybrid learning is an effective and computationally efficient approach to real-time electricity demand forecasting and operational grid management.