Traditional data-driven models for building cooling load prediction often fail during the initial operational stage due to insufficient training data, leading to unreliable forecasts. To address this challenge, a knowledge-data hybrid forecasting framework was proposed, it combines simplified heat-transfer-based load calculations with deep learning networks, where physics-based load estimates are embedded as auxiliary inputs to guide the data-driven predictor. Four models were evaluated, including three knowledge-embedded variants (using theoretical fresh-air load, envelope heat-transfer load, and their combination) and a purely data-driven CNN–LSTM baseline. Validated on an actual building under varying training-data richness, models based on the proposed framework reduce prediction errors by 39% to 69% and decrease error variance by nearly an order of magnitude compared with the baseline while effectively mitigating overfitting in small-sample scenarios. The computational simplicity and reliance only on easily accessible data underscore its practical value for early-stage engineering deployment.
Chen et al. (Thu,) studied this question.