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<p>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.</p>

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Deployable knowledge–data hybrid models for day-ahead cooling load prediction under data scarcity: a case study and performance validation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月26日

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  • Jian Chen,
  • Tao Sun,
  • Yuyan Zhang,
  • Wenjuan Zhang,
  • Lu Chen,
  • Weishang Liu,
  • Weijun Gao,
  • Tongyu Xu

摘要

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.