Short-Term Electricity Consumption Forecasting for a University Campus: A Hybrid SWT–Frequency Attention–TCN Approach

This study proposes a hybrid deep learning model based on the Stationary Wavelet Transform (SWT), a Frequency Attention mechanism, and a Temporal Convolutional Network architecture (TCN) for short-term electricity consumption forecasting at the university campus scale. The study utilizes a total of 40,896 observations collected from the central campus of Afyon Kocatepe University between 1 March 2024 and 30 April 2025, at 15 min intervals. Feature ablation analysis was conducted to determine the contribution of the candidate input variables. The results show that the inclusion of meteorological variables did not provide any additional improvement in forecasting accuracy. Therefore, the final compact model uses only historical electricity consumption together with the Hour and Day of Week calendar variables, which are known at the forecast origin. In the proposed model, the historical electricity consumption sequence is first decomposed into different frequency components using SWT, and the more informative frequency scales are adaptively weighted through the Frequency Attention mechanism. The resulting multiscale representation is then processed by the TCN architecture to model temporal dependencies and directly predict electricity consumption one hour ahead. Model performance was evaluated using the MAE, MSE, RMSE, MAPE, SMAPE, and R2 metrics under rolling and anchored walk-forward validation strategies. The proposed SWT + Frequency Attention + TCN model achieved the best overall performance among the evaluated models, with an RMSE of 10.614 and an R2 of 0.942 under the rolling walk-forward strategy and an RMSE of 10.286 and an R2 of 0.946 under the anchored walk-forward strategy. The findings demonstrate that the integration of SWT-based multiscale representation, selective frequency weighting, and TCN-based temporal dependency modeling provides an effective and robust framework for short-term campus electricity consumption forecasting.