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Experience knowledge decomposition – Data generation: Enhanced multi-step short-term cooling load predictions in data centres with data shortage issues

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  • Zhan, Lei
  • Li, Guannan
  • Xu, Chengliang
  • Ren, Haoshan
  • Sun, Yongjun

Abstract

For new data centres or data centres with inconsistent data interface protocols, the shortage of available cooling load data leads to low prediction accuracy in data-driven cooling load prediction (CLP) models. Data generation (DG) aims to enrich existing cooling data and can help address this shortage in data centre. However, as the prediction horizon extends, DG may become less effective, as the generated cooling load may not always be realistic for multiple future steps. To overcome this limitation, this study proposes a CLP strategy (EKD-DG) that combines DG with experience knowledge decomposition (EKD) to generate both dynamic and static cooling load. The original load is first decomposed into dynamic and static components using EKD. A conditional variational autoencoder (CVAE) is employed to process the dynamic load and generate synthetic dynamic cooling load with a similar distribution. The EKD-DG strategy is then trained using both the raw and the generated dynamic cooling load. Compared to DG, EKD-DG improves the quality of the generated data by producing more realistic dynamic cooling load.

Suggested Citation

  • Zhan, Lei & Li, Guannan & Xu, Chengliang & Ren, Haoshan & Sun, Yongjun, 2025. "Experience knowledge decomposition – Data generation: Enhanced multi-step short-term cooling load predictions in data centres with data shortage issues," Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:energy:v:328:y:2025:i:c:s0360544225021188
    DOI: 10.1016/j.energy.2025.136476
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