Learning-Based Precool Algorithms for Exploiting Foodstuff as Thermal Energy Reserve

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

doi:10.1109/TCST.2014.2328954

Learning-Based Precool Algorithms for Exploiting Foodstuff as Thermal Energy Reserve

Authors:

Vinther, Kasper ;
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0000-0003-2424-2402
Automation & Control, Department of Electronic Systems, The Technical Faculty of IT and Design, Aalborg University
Rasmussen, Henrik ;
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Automation & Control, Department of Electronic Systems, The Technical Faculty of IT and Design, Aalborg University
Izadi-Zamanabadi, Roozbeh ;
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The Faculty of Engineering and Science (TECH), Aalborg University
Stoustrup, Jakob ;
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0000-0001-9202-3135
Department of Electronic Systems, The Technical Faculty of IT and Design, Aalborg University
G. Alleyne, Andrew
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Department of Mechanical Science and Engineering, University of Illinois at Urbana Champaign

DOI:

10.1109/TCST.2014.2328954

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.

Type:

Journal article

Language:

English

Published in:

I E E E Transactions on Control Systems Technology, 2015, Vol 23, Issue 2, p. 557-569

Main Research Area:

Science/technology

Publication Status:

Published

Review type:

Peer Review

Submission year:

2015

Scientific Level:

Scientific

ID:

268626994

Learning-Based Precool Algorithms for Exploiting Foodstuff as Thermal Energy Reserve

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