Volume 59

Air Conditioning Load Prediction Based on Synergistic Feature Selection Hua Meng, Hao Sun, Saiwei Dai, Tingting Xu, Yuting Yao, Yingjun Ruan

Abstract

This study proposes a physics-informed feature selection framework to enhance the accuracy and interpretability of data-driven cooling load prediction models. Using operational data from a metro station HVAC system, we first identify temporal features through Pearson correlation analysis, followed by thermodynamic consistency screening to eliminate physically implausible candidates. Mutual information (MI) is then employed to quantify feature importance, while our Synergistic approach strategically incorporates physically critical but lower-MI features to improve model accuracy. Tested with LSTM models, the proposed method achieves a 5.6% improvement in prediction accuracy and 16–23% error reductions compared to conventional MI-based selection, with negligible computational overhead. Further validation with BPNN and diverse datasets confirmed robustness. By bridging data-driven metrics and domain-specific physics, this approach offers a balanced trade-off between accuracy and interpretability for building energy prediction.

Keywords synergistic feature selection, mutual information, physical redundancy, LSTM network, load prediction