Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats
Energy-saving is a mandatory research topic since the growing population demands additional energy yearly. Moreover, climate change requires more attention to reduce the impact of generating more CO<sub>2</sub>. As a result, some new research areas need to be explored to create innovativ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/17/6117 |
| _version_ | 1797495773011640320 |
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| author | Adán Medina Juana Isabel Méndez Pedro Ponce Therese Peffer Arturo Molina |
| author_facet | Adán Medina Juana Isabel Méndez Pedro Ponce Therese Peffer Arturo Molina |
| author_sort | Adán Medina |
| collection | DOAJ |
| description | Energy-saving is a mandatory research topic since the growing population demands additional energy yearly. Moreover, climate change requires more attention to reduce the impact of generating more CO<sub>2</sub>. As a result, some new research areas need to be explored to create innovative energy-saving alternatives in electrical devices that have high energy consumption. One research area of interest is the computer visual classification for reducing energy consumption and keeping thermal comfort in thermostats. Usually, connected thermostats obrtain information from sensors for detecting persons and scheduling autonomous operations to save energy. However, there is a lack of knowledge of how computer vision can be deployed in embedded digital systems to analyze clothing insulation in connected thermostats to reduce energy consumption and keep thermal comfort. The clothing classification algorithm embedded in a digital system for saving energy could be a companion device in connected thermostats to obtain the clothing insulation. Currently, there is no connected thermostat in the market using complementary computer visual classification systems to analyze the clothing insulation factor. Hence, this proposal aims to develop and evaluate an embedded real-time clothing classifier that could help to improve the efficiency of heating and ventilation air conditioning systems in homes or buildings. This paper compares six different one-stage object detection and classification algorithms trained with a small custom dataset in two embedded systems and a personal computer to compare the models. In addition, the paper describes how the classifier could interact with the thermostat to tune the temperature set point to save energy and keep thermal comfort. The results confirm that the proposed real-time clothing classifier could be implemented as a companion device in connected thermostats to provide additional information to end-users about making decisions on saving energy. |
| first_indexed | 2024-03-10T01:54:26Z |
| format | Article |
| id | doaj.art-d31e93ec8a014cafa1a87cd153abf20b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:54:26Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d31e93ec8a014cafa1a87cd153abf20b2023-11-23T13:00:34ZengMDPI AGEnergies1996-10732022-08-011517611710.3390/en15176117Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in ThermostatsAdán Medina0Juana Isabel Méndez1Pedro Ponce2Therese Peffer3Arturo Molina4Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, MexicoInstitute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, MexicoInstitute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, MexicoInstitute for Energy and Environment, University of California, Berkeley, CA 94720, USAInstitute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, MexicoEnergy-saving is a mandatory research topic since the growing population demands additional energy yearly. Moreover, climate change requires more attention to reduce the impact of generating more CO<sub>2</sub>. As a result, some new research areas need to be explored to create innovative energy-saving alternatives in electrical devices that have high energy consumption. One research area of interest is the computer visual classification for reducing energy consumption and keeping thermal comfort in thermostats. Usually, connected thermostats obrtain information from sensors for detecting persons and scheduling autonomous operations to save energy. However, there is a lack of knowledge of how computer vision can be deployed in embedded digital systems to analyze clothing insulation in connected thermostats to reduce energy consumption and keep thermal comfort. The clothing classification algorithm embedded in a digital system for saving energy could be a companion device in connected thermostats to obtain the clothing insulation. Currently, there is no connected thermostat in the market using complementary computer visual classification systems to analyze the clothing insulation factor. Hence, this proposal aims to develop and evaluate an embedded real-time clothing classifier that could help to improve the efficiency of heating and ventilation air conditioning systems in homes or buildings. This paper compares six different one-stage object detection and classification algorithms trained with a small custom dataset in two embedded systems and a personal computer to compare the models. In addition, the paper describes how the classifier could interact with the thermostat to tune the temperature set point to save energy and keep thermal comfort. The results confirm that the proposed real-time clothing classifier could be implemented as a companion device in connected thermostats to provide additional information to end-users about making decisions on saving energy.https://www.mdpi.com/1996-1073/15/17/6117energy savingclothing insulationembedded systemthermal comfortdeep learningcomputer vision |
| spellingShingle | Adán Medina Juana Isabel Méndez Pedro Ponce Therese Peffer Arturo Molina Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats Energies energy saving clothing insulation embedded system thermal comfort deep learning computer vision |
| title | Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats |
| title_full | Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats |
| title_fullStr | Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats |
| title_full_unstemmed | Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats |
| title_short | Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats |
| title_sort | embedded real time clothing classifier using one stage methods for saving energy in thermostats |
| topic | energy saving clothing insulation embedded system thermal comfort deep learning computer vision |
| url | https://www.mdpi.com/1996-1073/15/17/6117 |
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