An Internet of Things Framework to Forecast Indoor Air Quality Using Machine Learning

Rastogi, Krati; Lohani, Divya

doi:10.1007/978-981-15-4301-2_8

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…In another study, users' perception was an integral part of the index and did not rely on the measurable indicators alone [61]. In another study, the use of IoT was exercised by leveraging a blend of environmental parameters and an adaptive neuro-fuzzy interference system to improve computing accuracy [62]. These collective efforts highlight the growing recognition of the multifaceted nature of IAQ assessment and the evolving landscape of research aimed at improving indoor environmental conditions.…”

Section: Air Quality Indexmentioning

confidence: 99%

Enhanced Indoor Air Quality Dashboard Framework and Index for Higher Educational Institutions

Shoukry,

Goubran,

Tarabieh

2024

Buildings

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This research proposes a 10-step methodology for developing an enhanced IAQ dashboard and classroom index (CI) in higher educational facilities located in arid environments. The identified parameters of the enhanced IAQ dashboard–inspired by the pandemic experience, result from the literature review and the outcome of two electronic surveys of (52) respondents, including health professionals and facility management experts. On the other hand, the indicators included in the CI are based on (80) occupant survey responses, including parameters related to IAQ, Indoor Environmental Quality (IEQ), and thermal comfort, amongst other classroom operative considerations. The CI is further tested in four learning spaces at the American University in Cairo, Egypt. The main contribution of this research is to suggest a conceptual visualization of the dashboard and a practical classroom index that integrates a representative number of contextual indicators to recommend optimal IAQ scenarios for a given educational facility. This study concludes by highlighting several key findings: (1) both qualitative and quantitative metrics are necessary to capture indoor air quality-related parameters accurately; (2) tailoring the dashboard as well as the CI to specific contexts enhances its applicability across diverse locations; and finally, (3) the IAQ dashboard and CI offer flexibility for ad-hoc applications.

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Section: Air Quality Indexmentioning

confidence: 99%

Enhanced Indoor Air Quality Dashboard Framework and Index for Higher Educational Institutions

Shoukry,

Goubran,

Tarabieh

2024

Buildings

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“…Despite these advancements, gaps in the literature are evident. Some studies have developed forecast models for CO2 buildup without elaborating on real-time IEQ monitoring [69]. Others have explored innovative areas like integrating machine learning with EEG signals for predicting IEQ conditions based on occupants' brainwave patterns [70].…”

Section: Commercial Buildings: Real-time Ieq Control With Machine Lea...mentioning

confidence: 99%

Machine Learning Techniques in Indoor Environmental Quality Assessment

Kumar Gajendran,

Fazil Syed Ahmed Kabir,

Vadivelu

et al. 2024

Civil Engineering

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This chapter provides a comprehensive exploration of the evolving role of machine learning in Indoor Environmental Quality (IEQ) assessment. As urban living spaces become increasingly enclosed, the importance of maintaining optimal IEQ for human health and well-being has surged. Traditional methods for IEQ assessment, while effective, often fail to provide real-time monitoring and control. This gap is increasingly being addressed by the integration of machine learning techniques, allowing for enhanced predictive modeling, real-time optimization, and robust anomaly detection. The chapter delves into a comparative analysis of various machine learning techniques including supervised, unsupervised, and reinforcement learning, demonstrating their unique benefits in IEQ assessment. Practical implementations of these techniques in residential, commercial, and specialized environments are further illustrated through detailed case studies. The chapter also addresses the existing challenges in implementing machine learning for IEQ assessment and provides an outlook on future trends and potential research directions. The comprehensive review offered in this chapter encourages continued innovation and research in leveraging machine learning. for more efficient and effective IEQ assessment.

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“…Some studies show that when the concentration of carbon dioxide in indoor air increases to a moderate level, headache, fatigue, and inattention will be caused, while higher carbon dioxide concentration will lead to vomiting, dizziness, and other symptoms [106]. Therefore, it is an important monitoring content in indoor environments, such as school buildings and offices [24,39]. The monitoring of PM has received increasing attention due to the dramatic impact of adverse weather in recent years, such as haze, and no exception is made in indoor air monitoring systems.…”

Section: Sensorsmentioning

confidence: 99%

Review of Communication Technology in Indoor Air Quality Monitoring System and Challenges

Zhao

Yang

2022

Electronics

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In the past decade, with the development of communication technology, indoor air quality (IAQ) monitoring technology has made significant progress, which improves the real-time performance of the monitoring system, and reduces potential impacts on health-related pollutants. To investigate and analyze the development of IAQ monitoring technology and the application of communication technology in this field, this review conducts a comprehensive search, collation, and analysis of the literature in the relevant fields over the past decade, and reviews 91 articles from the usage scenarios, monitoring parameters, and communication technology of the IAQ monitoring system. The review shows the development and main research direction of IAQ monitoring, and focuses on the selection and application of communication technology in different projects. In addition, this study also discusses the problems and challenges in the IAQ monitoring system, which provides a reference for researchers and promotes the rapid and all-around development of IAQ monitoring.

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An Internet of Things Framework to Forecast Indoor Air Quality Using Machine Learning

Cited by 12 publications

References 9 publications

Enhanced Indoor Air Quality Dashboard Framework and Index for Higher Educational Institutions

Enhanced Indoor Air Quality Dashboard Framework and Index for Higher Educational Institutions

Machine Learning Techniques in Indoor Environmental Quality Assessment

Review of Communication Technology in Indoor Air Quality Monitoring System and Challenges

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