Residual Learning Enhanced Grey-Box Modelling for Indoor Temperature Prediction and IEQ Assessment

Abstract

The increasing demand for the energy-efficient and occupant-centred operation of educational buildings requires accurate and interpretable models capable of predicting indoor environmental conditions under real operating constraints. This study proposes a residual learning-enhanced grey-box modelling framework for predicting indoor air temperature and assessing indoor environmental quality indicators in a KNX-enabled educational building operating under simple thermostatic heating control. The approach combines a reduced-order discrete-time RC thermal model with a data-driven machine learning component trained to model the next-step residual between measured and simulated indoor temperatures. High-resolution KNX monitoring data were recorded at a 5 min sampling interval over three consecutive months (October–December) during the heating season. Using a chronological 70/30 train–test split, the identified RC grey-box model achieved a pooled test RMSE of 0.269 °C, an MAE of 0.126 °C, and an R2 of 0.987. The proposed hybrid formulation achieved RMSE = 0.343 °C, MAE = 0.106 °C, and R2 = 0.978 across 62,456 test samples. While the pooled RMSE remains influenced by occasional larger deviations in a small number of rooms, the hybrid model yields a consistent reduction in absolute error (≈16% MAE reduction) and reduced inter-room variability compared to the physics-based baseline. These results indicate that residual learning can enhance predictive robustness under decentralized thermostatic operation and limited sensing, while preserving physical interpretability. The proposed framework provides a practical and scalable solution for indoor temperature prediction and IEQ assessment in educational buildings using existing KNX automation data.