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An adaptive grey-box model for nearly zero energy building operations in challenging climate

Author

Listed:
  • Wang, Xiaopeng
  • Ye, Yihang
  • Deng, Wu
  • Wu, Yupeng
  • Zhang, Zhiang
  • Wu, Tao
  • Ma, Yuanli

Abstract

Grey-box model predictive control (MPC) can utilize building thermal mass to act as “thermal batteries” in nearly zero-energy buildings (NZEBs). However, conventional grey-box models demonstrate limited reliability under rapid weather changes. Specifically, conventional adaptive (CA) models trained on sequential historical data lack representativeness during extreme weather conditions, reducing predictive accuracy. This study proposes a Weather Similarity-based Adaptive (WSA) method for grey-box model calibration through similarity-based training dataset selection. The WSA method identifies representative historical conditions using key meteorological variables. Both predictive and control performance of the proposed method in an NZEB were evaluated using a high-fidelity co-simulation testbed across multiple climate scenarios, including analysis of hyperparameter influences.

Suggested Citation

  • Wang, Xiaopeng & Ye, Yihang & Deng, Wu & Wu, Yupeng & Zhang, Zhiang & Wu, Tao & Ma, Yuanli, 2026. "An adaptive grey-box model for nearly zero energy building operations in challenging climate," Energy, Elsevier, vol. 355(C).
    • Handle: RePEc:eee:energy:v:355:y:2026:i:c:s0360544226012569
    DOI: 10.1016/j.energy.2026.141151
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