Learning-based precool algorithms for exploiting foodstuff as thermal energy reserve

Kasper Vinther, Henrik Rasmussen, Roozbeh Izadi-Zamanabadi, Jakob Stoustrup, Andrew G. Alleyne

Research output: Contribution to journalArticle

Abstract

Refrigeration is important to sustain high foodstuff quality and lifetime. Keeping the foodstuff within temperature thresholds in supermarkets is also important due to legislative requirements. Failure to do so can result in discarded foodstuff, a penalty fine to the shop owner, and health issues. However, the refrigeration system might not be dimensioned to cope with hot summer days or performance degradation over time. Two learning-based algorithms are therefore proposed for thermostatically controlled loads, which precools the foodstuff in display cases in an anticipatory manner based on how saturated the system has been in recent days. A simulation model of a supermarket refrigeration system is provided and evaluation of the precool strategies shows that negative thermal energy can be stored in foodstuff to cope with saturation. A system model or additional hardware is not required, which makes the algorithms easy to implement in existing systems.

Original languageEnglish (US)
Article number6844010
Pages (from-to)557-569
Number of pages13
JournalIEEE Transactions on Control Systems Technology
Volume23
Issue number2
DOIs
StatePublished - Mar 1 2015

Keywords

  • Control systems
  • learning
  • precool
  • refrigeration
  • temperature control
  • thermal storage

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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