A Wireless Indoor Environmental Quality Logger Processing the Indoor Global Comfort Index
Indoor environmental quality (IEQ) has a high-level of impact on one’s health and productivity. It is widely accepted that IEQ is composed of four categories: thermal comfort, indoor air quality (IAQ), visual comfort, and acoustic comfort. The main physical parameters that primarily represent these...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2558 |
| _version_ | 1797437699982884864 |
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| author | Stefano Riffelli |
| author_facet | Stefano Riffelli |
| author_sort | Stefano Riffelli |
| collection | DOAJ |
| description | Indoor environmental quality (IEQ) has a high-level of impact on one’s health and productivity. It is widely accepted that IEQ is composed of four categories: thermal comfort, indoor air quality (IAQ), visual comfort, and acoustic comfort. The main physical parameters that primarily represent these comfort categories can be monitored using sensors. To this purpose, the article proposes a wireless indoor environmental quality logger. In the literature, global comfort indices are often assessed objectively (using sensors) or subjectively (through surveys). This study adopts an integrated approach that calculates a predicted indoor global comfort index (P-IGCI) using sensor data and estimates a real perceived indoor global comfort index (RP-IGCI) based on questionnaires. Among the 19 different tested algorithms, the stepwise multiple linear regression model minimized the distance between the two comfort indices. In the case study involving a university classroom setting—thermal comfort and indoor air quality were identified as the most relevant IEQ elements from a subjective point of view. The model also confirms this findings from an objective perspective since temperature and CO<sub>2</sub> merge as the measured physical parameters with the most impacts on overall comfort. |
| first_indexed | 2024-03-09T11:26:24Z |
| format | Article |
| id | doaj.art-3a3b377b9d3846e1ae2d15b7695007c0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:26:24Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3a3b377b9d3846e1ae2d15b7695007c02023-12-01T00:00:51ZengMDPI AGSensors1424-82202022-03-01227255810.3390/s22072558A Wireless Indoor Environmental Quality Logger Processing the Indoor Global Comfort IndexStefano Riffelli0Department of Applied and Pure Sciences (DiSPeA), University of Urbino Carlo Bo, 61029 Urbino, ItalyIndoor environmental quality (IEQ) has a high-level of impact on one’s health and productivity. It is widely accepted that IEQ is composed of four categories: thermal comfort, indoor air quality (IAQ), visual comfort, and acoustic comfort. The main physical parameters that primarily represent these comfort categories can be monitored using sensors. To this purpose, the article proposes a wireless indoor environmental quality logger. In the literature, global comfort indices are often assessed objectively (using sensors) or subjectively (through surveys). This study adopts an integrated approach that calculates a predicted indoor global comfort index (P-IGCI) using sensor data and estimates a real perceived indoor global comfort index (RP-IGCI) based on questionnaires. Among the 19 different tested algorithms, the stepwise multiple linear regression model minimized the distance between the two comfort indices. In the case study involving a university classroom setting—thermal comfort and indoor air quality were identified as the most relevant IEQ elements from a subjective point of view. The model also confirms this findings from an objective perspective since temperature and CO<sub>2</sub> merge as the measured physical parameters with the most impacts on overall comfort.https://www.mdpi.com/1424-8220/22/7/2558indoor environmental quality (IEQ) loggerRaspberry PisensorsInternet of Things (IoT) thermal comfortindoor air quality (IAQ)visual comfort |
| spellingShingle | Stefano Riffelli A Wireless Indoor Environmental Quality Logger Processing the Indoor Global Comfort Index Sensors indoor environmental quality (IEQ) logger Raspberry Pi sensors Internet of Things (IoT) thermal comfort indoor air quality (IAQ) visual comfort |
| title | A Wireless Indoor Environmental Quality Logger Processing the Indoor Global Comfort Index |
| title_full | A Wireless Indoor Environmental Quality Logger Processing the Indoor Global Comfort Index |
| title_fullStr | A Wireless Indoor Environmental Quality Logger Processing the Indoor Global Comfort Index |
| title_full_unstemmed | A Wireless Indoor Environmental Quality Logger Processing the Indoor Global Comfort Index |
| title_short | A Wireless Indoor Environmental Quality Logger Processing the Indoor Global Comfort Index |
| title_sort | wireless indoor environmental quality logger processing the indoor global comfort index |
| topic | indoor environmental quality (IEQ) logger Raspberry Pi sensors Internet of Things (IoT) thermal comfort indoor air quality (IAQ) visual comfort |
| url | https://www.mdpi.com/1424-8220/22/7/2558 |
| work_keys_str_mv | AT stefanoriffelli awirelessindoorenvironmentalqualityloggerprocessingtheindoorglobalcomfortindex AT stefanoriffelli wirelessindoorenvironmentalqualityloggerprocessingtheindoorglobalcomfortindex |