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Model predictive control incorporating data correction for LHTES power controlling: Deployment and case study in data center

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  • Gao, Jiacheng
  • Lv, Yanlong
  • Feng, Lejun
  • Sui, Jun
  • Jin, Hongguang

Abstract

Latent Heat Thermal Energy Storage (LHTES) can effectively reduce cooling energy consumption in data centers through renewable energy utilization and peak load management. However, the lack of practical discharging power control methods for real-world engineering applications has hindered their widespread adoption. To address this challenge, this study used the model predictive control (MPC) strategy incorporating data correction to solve the power control challenges of LHTES, and validated in a data center cooling system retrofit project.

Suggested Citation

  • Gao, Jiacheng & Lv, Yanlong & Feng, Lejun & Sui, Jun & Jin, Hongguang, 2025. "Model predictive control incorporating data correction for LHTES power controlling: Deployment and case study in data center," Applied Energy, Elsevier, vol. 401(PA).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pa:s030626192501390x
    DOI: 10.1016/j.apenergy.2025.126660
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