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

IF 3 4区 工程技术 Q3 ENERGY & FUELS
Energies Pub Date : 2024-01-03 DOI:10.3390/en17010243
M. Benzaama, Karima Touati, Y. El Mendili, Malo Le Guern, F. Streiff, Steve Goodhew
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引用次数: 0

摘要

发达国家的人口在室内度过大量时间,而恶劣的室内空气质量(IAQ)对人类健康的影响是巨大的。许多因接触室内空气污染物而过早死亡的人都是因为室内空气不佳导致疾病加重。二氧化碳是这些污染物中最常见的一种,通常也是室内空气质量的一个指标。由于人类的呼吸和活动,室内的二氧化碳浓度可能大大高于室外水平。这项研究的主要目的是通过 CobBauge 原型,特别是在建筑交付使用后的头几个月,以数值方式研究水泥砌块建筑中的室内相对湿度和二氧化碳。为此进行了现场实验研究和使用人工神经网络进行数值预测。该研究介绍了如何使用片断自回归外生模型(PWARX)来预测一栋由椰壳纤维和轻质土构成的双层墙体建筑的室内相对湿度(RH)和二氧化碳含量。该模型通过 27 天内收集的实验数据进行了验证,在此期间测量了室内相对湿度和二氧化碳含量以及外部条件。结果表明,PWARX 模型准确地预测了相对湿度水平,并根据材料内的含水量和外部条件将其分为不同的状态。然而,虽然该模型准确预测了室内二氧化碳水平,但由于影响室内环境二氧化碳水平的因素错综复杂,因此在对其进行精细分类时面临挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Energies
Energies ENERGY & FUELS-
CiteScore
6.20
自引率
21.90%
发文量
8045
审稿时长
1.9 months
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.
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