ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse
| Autor: | Armin Hafner, Mohan Kolhe, Sven Myrdahl Opalic, Henrik Kofoed Nielsen, Ángel Á. Pardiñas, Morten Goodwin, Lei Jiao |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Renewable Energy
Sustainability and the Environment Computer science business.industry 020209 energy Strategy and Management 05 social sciences 02 engineering and technology Energy consumption Industrial and Manufacturing Engineering Energy storage Automotive engineering Renewable energy Refrigerant Energy management system Mean absolute percentage error Operating temperature 050501 criminology 0202 electrical engineering electronic engineering information engineering Water cooling business VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 0505 law General Environmental Science |
| Zdroj: | Journal of Cleaner Production |
| Popis: | Author's accepted manuscript Industrial cooling systems consume large quantities of energy with highly variable power demand. To reduce environmental impact and overall energy consumption, and to stabilize the power requirements, it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To control these operations continuously in a complex energy system, an intelligent energy management system can be employed using operational data and machine learning. In this work, we have developed an artificial neural network based technique for modelling operational CO2 refrigerant based industrial cooling systems for embedding in an overall energy management system. The operating temperature and pressure measurements, as well as the operating frequency of compressors, are used in developing operational model of the cooling system, which outputs electrical consumption and refrigerant mass flow without the need for additional physical measurements. The presented model is superior to a generalized theoretical model, as it learns from data that includes individual compressor type characteristics. The results show that the presented approach is relatively precise with a Mean Average Percentage Error (MAPE) as low as 5%, using low resolution and asynchronous data from a case study system. The developed model is also tested in a laboratory setting, where MAPE is shown to be as low as 1.8%. |
| Databáze: | OpenAIRE |
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