Machine Learning Techniques in Indoor Environmental Quality Assessment

Mohan Kumar Gajendran, Ijaz Fazil Syed Ahmed Kabir, Eddie Eddie Yin-Kwee Ng, Sudhakar Vadivelu and Ravi Chandra Thota

A chapter in Advancements in Indoor Environmental Quality and Health from IntechOpen

Abstract: 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.

Keywords: indoor environmental quality; machine learning; IEQ assessment; predictive modeling; supervised learning; unsupervised learning; reinforcement learning; real-time control; anomaly detection; residential buildings; commercial buildings; future trends (search for similar items in EconPapers)
JEL-codes: Q53 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:318227

DOI: 10.5772/intechopen.114012

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