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Improvements in the prediction of energy performance of refrigeration systems.
Number: 1026
Author(s) : PEARSON A.
Summary
The prediction of energy performance based on minimal datasets is an important technique in the reduction of energy use in warehouses because it enables rapid feedback on the effectiveness of maintenance interventions and provides early warning of adverse changes in operating efficiency. However large daily variations in the kWh used by the refrigeration plant, which cannot be explained by ambient temperature variation or building throughput, mean that the first assessments after the maintenance intervention may be very inaccurate. An algorithm which accounts for seasonal variation has been developed and previously presented. This paper examines some possible sources of daily variation in kWh figures for distribution warehouses and uses large datasets from a significant number of sites recorded over several years to look for correlation
between factors. Recommendations on methods of data capture for new and existing facilities are presented and some observations on the design of new buildings are offered. This includes some insight into the relative performance of various natural refrigerant solutions.
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Details
- Original title: Improvements in the prediction of energy performance of refrigeration systems.
- Record ID : 30027894
- Languages: English
- Source: 14th IIR-Gustav Lorentzen Conference on Natural Refrigerants (GL2020). Proceedings. Kyoto, Japon, December 7-9th 2020.
- Publication date: 2020/12/07
- DOI: http://dx.doi.org/10.18462/iir.gl.2020.1026
- Document available for consultation in the library of the IIR headquarters only.
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