CNN-ResLSTM-ConNet: A Novel Hybrid Deep Learning Model for Peak Electricity Demand Forecasting of National Grid

Precise forecasting of daily peak electricity demand is fundamental for ensuring the stability, economic operation, and strategic planning of a national power grid. Conventional statistical models frequently struggle to capture the complex, non-linear patterns inherent in grid load data, which are influenced by temporal dependencies, seasonality, and external factors. While deep learning offers a promising alternative, existing models frequently lack the integrated architecture to simultaneously extract spatial features and model long-term temporal dependencies effectively. This study proposes a novel hybrid deep learning model, termed CNN-ResLSTM-ConNet, to address this gap for forecasting the daily evening peak electricity demand of the Bangladesh national power grid. The model innovatively combines Convolutional Neural Networks (CNN) for extracting salient spatial features, Long Short-Term Memory (LSTM) networks with residual connections (ResLSTM) to capture long-term temporal patterns and mitigate vanishing gradient problems, and a concatenation network that synergistically integrates these features for final prediction. In this research, a comprehensive forecasting solution is presented using the historic data of the daily electricity demand of the power grid from 2016 to 2024. Its performance was benchmarked against a suite of models, including traditional statistical methods (ARIMA, ETS), base deep learning models (MLP, CNN, LSTM), and a state-of-the-art hybrid model (LSTM-mTrans-MLP). The proposed CNN-ResLSTM-ConNet model demonstrated better performance, achieving the lowest error rates across all metrics, including a Root Mean Square Error (RMSE) of 967.30, a Mean Absolute Error (MAE) of 657.08, and a Mean Absolute Percentage Error (MAPE) of 5.45%. These results signify a substantial improvement over all benchmarks, confirming the efficacy of the hybrid architecture. We conclude that the CNN-ResLSTM-ConNet model provides a robust and accurate tool for peak demand forecasting, with the potential to enhance grid reliability, optimize energy dispatch, and inform capacity planning for power utilities.