An Integrated regARIMA–HP Filter–CNN Framework with Attention Mechanism for Monthly Electricity Demand Forecasting

Monthly electricity demand forecasts are often affected by outliers and moving-holiday effects. This study proposes a forecasting framework that integrates regression with ARIMA errors (regARIMA), Hodrick–Prescott (HP) filter decomposition, and a multi-branch convolutional neural network (CNN) with channel attention. The regARIMA model removes outlier and moving-holiday effects; the HP filter separates the adjusted series into trend and cyclical components; separate CNNs forecast these components; and the final forecast is reconstructed with moving-holiday correction. On the primary Changzhou dataset, the framework achieved the lowest two-year average RMSE (2.44), MAE (1.89), and MAPE (3.36%) and one of the highest R2 values (0.91). In Guangzhou, it ranked second in the two-year averages of all four reported metrics. The top-ranked model retained the proposed X13-HP preprocessing and multi-scale CNN-attention core but added two bidirectional long short-term memory (Bi-LSTM) layers with self-attention. Per-comparison tests showed no statistically significant difference between this extended model and the proposed framework, and no multiplicity adjustment was applied. By contrast, a Bi-LSTM and self-attention model without the multi-scale CNN front end performed poorly. These results indicate that the main advantage of the proposed design lies in multi-scale convolutional feature extraction with channel attention, whereas recurrent depth alone is insufficient. The framework therefore provides accurate, stable, and structurally simpler forecasting.