M. Benzaama, Karima Touati, Y. El Mendili, Malo Le Guern, F. Streiff, Steve Goodhew
{"title":"基于机器学习的室内相对湿度和二氧化碳识别(使用分段自回归外生模型):Cob 原型研究","authors":"M. Benzaama, Karima Touati, Y. El Mendili, Malo Le Guern, F. Streiff, Steve Goodhew","doi":"10.3390/en17010243","DOIUrl":null,"url":null,"abstract":"The population of developed nations spends a significant amount of time indoors, and the implications of poor indoor air quality (IAQ) on human health are substantial. Many premature deaths attributed to exposure to indoor air pollutants result from diseases exacerbated by poor indoor air. CO2, one of these pollutants, is the most prevalent and often serves as an indicator of IAQ. Indoor CO2 concentrations can be significantly higher than outdoor levels due to human respiration and activity. The primary objective of this research was to numerically investigate the indoor relative humidity and CO2 in cob buildings through the CobBauge prototype, particularly during the first months following the building delivery. Both in situ experimental studies and numerical predictions using an artificial neural network were conducted for this purpose. The study presented the use of a piecewise autoregressive exogenous model (PWARX) for indoor relative humidity (RH) and CO2 content in a building constructed with a double walling system consisting of cob and light earth. The model was validated using experimental data collected over a 27-day period, during which indoor RH and CO2 levels were measured alongside external conditions. The results indicate that the PWARX model accurately predicted RH levels and categorized them into distinct states based on moisture content within materials and external conditions. However, while the model accurately predicted indoor CO2 levels, it faced challenges in finely classifying them due to the complex interplay of factors influencing CO2 levels in indoor environments.","PeriodicalId":11557,"journal":{"name":"Energies","volume":"22 6","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study\",\"authors\":\"M. Benzaama, Karima Touati, Y. El Mendili, Malo Le Guern, F. Streiff, Steve Goodhew\",\"doi\":\"10.3390/en17010243\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The population of developed nations spends a significant amount of time indoors, and the implications of poor indoor air quality (IAQ) on human health are substantial. Many premature deaths attributed to exposure to indoor air pollutants result from diseases exacerbated by poor indoor air. CO2, one of these pollutants, is the most prevalent and often serves as an indicator of IAQ. Indoor CO2 concentrations can be significantly higher than outdoor levels due to human respiration and activity. The primary objective of this research was to numerically investigate the indoor relative humidity and CO2 in cob buildings through the CobBauge prototype, particularly during the first months following the building delivery. Both in situ experimental studies and numerical predictions using an artificial neural network were conducted for this purpose. The study presented the use of a piecewise autoregressive exogenous model (PWARX) for indoor relative humidity (RH) and CO2 content in a building constructed with a double walling system consisting of cob and light earth. The model was validated using experimental data collected over a 27-day period, during which indoor RH and CO2 levels were measured alongside external conditions. The results indicate that the PWARX model accurately predicted RH levels and categorized them into distinct states based on moisture content within materials and external conditions. However, while the model accurately predicted indoor CO2 levels, it faced challenges in finely classifying them due to the complex interplay of factors influencing CO2 levels in indoor environments.\",\"PeriodicalId\":11557,\"journal\":{\"name\":\"Energies\",\"volume\":\"22 6\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-01-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energies\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/en17010243\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energies","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/en17010243","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study
The population of developed nations spends a significant amount of time indoors, and the implications of poor indoor air quality (IAQ) on human health are substantial. Many premature deaths attributed to exposure to indoor air pollutants result from diseases exacerbated by poor indoor air. CO2, one of these pollutants, is the most prevalent and often serves as an indicator of IAQ. Indoor CO2 concentrations can be significantly higher than outdoor levels due to human respiration and activity. The primary objective of this research was to numerically investigate the indoor relative humidity and CO2 in cob buildings through the CobBauge prototype, particularly during the first months following the building delivery. Both in situ experimental studies and numerical predictions using an artificial neural network were conducted for this purpose. The study presented the use of a piecewise autoregressive exogenous model (PWARX) for indoor relative humidity (RH) and CO2 content in a building constructed with a double walling system consisting of cob and light earth. The model was validated using experimental data collected over a 27-day period, during which indoor RH and CO2 levels were measured alongside external conditions. The results indicate that the PWARX model accurately predicted RH levels and categorized them into distinct states based on moisture content within materials and external conditions. However, while the model accurately predicted indoor CO2 levels, it faced challenges in finely classifying them due to the complex interplay of factors influencing CO2 levels in indoor environments.
期刊介绍:
Energies (ISSN 1996-1073) is an open access journal of related scientific research, technology development and policy and management studies. It publishes reviews, regular research papers, and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.