Research on Short-Term Energy Consumption Forecasting for Cold Regions Based on the TCN–Transformer Model

Energy consumption accounts for a significant proportion of China’s building operations, exhibiting notable regional variations influenced by geographic characteristics. Factors affecting building energy consumption during transitional seasons are particularly complex in severely cold regions. This study selected a university library in Hohhot, Inner Mongolia Autonomous Region, as its research subject, employing a hybrid TCN–transformer model to conduct predictive experiments on short-term building energy consumption. We first collected environmental data from Hohhot’s spring–summer transitional period. Following parameter screening and preprocessing, this dataset was input into the TCN–transformer model. By integrating TCN with transformer’s self-attention mechanism, the model addresses the region’s high noise levels and non-stationarity, enabling precise forecasting. To validate the effectiveness of the proposed model, a comparative analysis was conducted against traditional models, namely SVR and LSTM, on the same dataset. The results demonstrated that TCN–transformer achieves superior comprehensive performance, evidenced by a higher prediction accuracy (R2 = 0.8765) and lower error (MAE = 0.24603, RMSE = 0.32829), outperforming the baseline models. This research provides an innovative and efficient hybrid modelling approach and technical methodology for predicting building energy consumption during transitional seasons in severely cold regions, holding positive implications for enhancing building energy efficiency and promoting sustainable development.