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Low-Cost CO₂ NDIR Sensors: Performance Evaluation and Calibration Using Machine Learning Techniques.

ABSTRACT: The study comprehensively evaluates low-cost CO₂ sensors from different price tiers, assessing their performance against a reference-grade instrument and exploring the possibility of calibration using different machine learning techniques. Three sensors (Sunrise AB by Senseair, K30 CO₂ by Senseair, and GMP 343 by Vaisala) were tested alongside a reference instrument (Los Gatos precision greenhouse gas analyzer). The results revealed differences in sensor performance, with the higher cost Vaisala sensors exhibiting superior accuracy. Despite its lower price, the Sunrise sensors still demonstrated reasonable accuracy. Meanwhile, the K30 sensor measurements displayed higher variability and noise. Machine learning models, including linear regression, gradient boosting reg

SUBMITTER: Dubey R

PROVIDER: S-EPMC11397870 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Low-Cost CO₂ NDIR Sensors: Performance Evaluation and Calibration Using Machine Learning Techniques.

OmicsDI is part of the ELIXIR infrastructure

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Low-Cost CO2 NDIR Sensors: Performance Evaluation and Calibration Using Machine Learning Techniques.

OmicsDI is part of the ELIXIR infrastructure

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Low-Cost CO₂ NDIR Sensors: Performance Evaluation and Calibration Using Machine Learning Techniques.