Research on HVAC Energy Consumption Prediction Based on TCN-BiGRU-Attention

HVAC (Heating, Ventilation and Air Conditioning) system in buildings is a major component of energy consumption, and realizing high-precision energy consumption prediction is of great significance for intelligent building management. Aiming at the problems of insufficient modeling ability of nonlinear features and insufficient portrayal of long time-series dependencies in prediction methods, this paper proposes an HVAC energy consumption prediction model that combines time-sequence convolutional network (TCN), bi-directional gated recurrent unit (BiGRU), and Attention mechanism. The model takes advantage of TCN’s parallel computing and multi-scale feature extraction, BiGRU’s bidirectional temporal dependency modeling, and Attention’s weight assignment of key features to effectively improve the prediction accuracy. In this work, the HVAC load is represented by the building-level electricity meter readings of office buildings equipped with centralized, electrically driven heating, ventilation, and air-conditioning systems. Therefore, the proposed method is mainly applicable to building-level HVAC energy consumption prediction scenarios where aggregated hourly electricity or cooling energy measurements are available, rather than to the control of individual terminal units. The experimental results show that the model in this paper achieves better performance compared to the method on ASHRAE dataset, the proposed model outperforms the baseline by 2.3%, 22.2%, and 34.7% in terms of MAE, RMSE, and MAPE, respectively, on the one-year time-by-time data of the office building, and meanwhile it is significant 54.1% on the MSE metrics.