A Study on the Prediction of Electrical Energy in Food Storage Using Machine Learning
This study discusses methods for the sustainability of freezers used in frozen storage methods known as long-term food storage methods. Freezing preserves the quality of food for a long time. However, it is inevitable to use a freezer that uses a large amount of electricity to store food with this m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/1/346 |
| _version_ | 1797626264715001856 |
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| author | Sangoh Kim |
| author_facet | Sangoh Kim |
| author_sort | Sangoh Kim |
| collection | DOAJ |
| description | This study discusses methods for the sustainability of freezers used in frozen storage methods known as long-term food storage methods. Freezing preserves the quality of food for a long time. However, it is inevitable to use a freezer that uses a large amount of electricity to store food with this method. To maintain the quality of food, lower temperatures are required, and therefore more electrical energy must be used. In this study, machine learning was performed using data obtained through a freezer test, and an optimal inference model was obtained with this data. If the inference model is applied to the selection of freezer control parameters, it turns out that optimal food storage is possible using less electrical energy. In this paper, a method for obtaining a dataset for machine learning in a deep freezer and the process of performing SLP and MLP machine learning through the obtained dataset are described. In addition, a method for finding the optimal efficiency is presented by comparing the performances of the inference models obtained in each method. The application of such a development method can reduce electrical energy in the food manufacturing equipment related industry, and accordingly it will be possible to achieve carbon emission reductions. |
| first_indexed | 2024-03-11T10:07:53Z |
| format | Article |
| id | doaj.art-761af1af4ace4fd3ab4ff791eb38a9f1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:07:53Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-761af1af4ace4fd3ab4ff791eb38a9f12023-11-16T14:55:47ZengMDPI AGApplied Sciences2076-34172022-12-0113134610.3390/app13010346A Study on the Prediction of Electrical Energy in Food Storage Using Machine LearningSangoh Kim0Department of Plant and Food Sciences, Sangmyung University, Cheonan 31066, Republic of KoreaThis study discusses methods for the sustainability of freezers used in frozen storage methods known as long-term food storage methods. Freezing preserves the quality of food for a long time. However, it is inevitable to use a freezer that uses a large amount of electricity to store food with this method. To maintain the quality of food, lower temperatures are required, and therefore more electrical energy must be used. In this study, machine learning was performed using data obtained through a freezer test, and an optimal inference model was obtained with this data. If the inference model is applied to the selection of freezer control parameters, it turns out that optimal food storage is possible using less electrical energy. In this paper, a method for obtaining a dataset for machine learning in a deep freezer and the process of performing SLP and MLP machine learning through the obtained dataset are described. In addition, a method for finding the optimal efficiency is presented by comparing the performances of the inference models obtained in each method. The application of such a development method can reduce electrical energy in the food manufacturing equipment related industry, and accordingly it will be possible to achieve carbon emission reductions.https://www.mdpi.com/2076-3417/13/1/346artificial intelligencemachine learningfood storageelectrical energy optimizationelectrical energy prediction |
| spellingShingle | Sangoh Kim A Study on the Prediction of Electrical Energy in Food Storage Using Machine Learning Applied Sciences artificial intelligence machine learning food storage electrical energy optimization electrical energy prediction |
| title | A Study on the Prediction of Electrical Energy in Food Storage Using Machine Learning |
| title_full | A Study on the Prediction of Electrical Energy in Food Storage Using Machine Learning |
| title_fullStr | A Study on the Prediction of Electrical Energy in Food Storage Using Machine Learning |
| title_full_unstemmed | A Study on the Prediction of Electrical Energy in Food Storage Using Machine Learning |
| title_short | A Study on the Prediction of Electrical Energy in Food Storage Using Machine Learning |
| title_sort | study on the prediction of electrical energy in food storage using machine learning |
| topic | artificial intelligence machine learning food storage electrical energy optimization electrical energy prediction |
| url | https://www.mdpi.com/2076-3417/13/1/346 |
| work_keys_str_mv | AT sangohkim astudyonthepredictionofelectricalenergyinfoodstorageusingmachinelearning AT sangohkim studyonthepredictionofelectricalenergyinfoodstorageusingmachinelearning |