1 Automation & Control, The Faculty of Engineering and Science (ENG), Aalborg University, VBN2 Department of Electronic Systems, The Faculty of Engineering and Science (ENG), Aalborg University, VBN3 The Faculty of Engineering and Science (TECH), Aalborg University, VBN4 Aalborg U Robotics, The Faculty of Humanities, Aalborg University, VBN5 Strategic Research Centre on Zero Energy Buildings, The Faculty of Engineering and Science (ENG), Aalborg University, VBN6 Aalborg University Space Center, The Faculty of Engineering and Science (ENG), Aalborg University, VBN7 Department of Mechanical Science and Engineering, University of Illinois at Urbana Champaign
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.
I E E E Transactions on Control Systems Technology, 2015, Vol 23, Issue 2, p. 557-569