IoT-Based Unsupervised Learning for Characterizing Laboratory Operational States to Improve Safety and Sustainability

Bibars Amangeldy, Timur Imankulov (), Nurdaulet Tasmurzayev (), Baglan Imanbek, Gulmira Dikhanbayeva and Yedil Nurakhov
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Bibars Amangeldy: LLP «DigitAlem», Almaty 050042, Kazakhstan
Timur Imankulov: LLP «DigitAlem», Almaty 050042, Kazakhstan
Nurdaulet Tasmurzayev: LLP «DigitAlem», Almaty 050042, Kazakhstan
Baglan Imanbek: Faculty of Information Technologies and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan
Gulmira Dikhanbayeva: LLP «DigitAlem», Almaty 050042, Kazakhstan
Yedil Nurakhov: LLP «DigitAlem», Almaty 050042, Kazakhstan

Sustainability, 2025, vol. 17, issue 18, 1-26

Abstract: Laboratory buildings represent some of the highest energy-consuming infrastructure due to stringent environmental requirements and the continuous operation of specialized equipment. Ensuring both energy efficiency and indoor air quality (IAQ) in such spaces remains a central challenge for sustainable building design and operation. Recent advances in Internet of Things (IoT) systems allow for real-time monitoring of multivariate environmental parameters, including CO 2 , total volatile organic compounds (TVOC), PM 2.5 , temperature, humidity, and noise. However, these datasets are often noisy or incomplete, complicating conventional monitoring approaches. Supervised anomaly detection methods are ill-suited to such contexts due to the lack of labeled data. In contrast, unsupervised machine learning (ML) techniques can autonomously detect patterns and deviations without annotations, offering a scalable alternative. The challenge of identifying anomalous environmental conditions and latent operational states in laboratory environments is addressed through the application of unsupervised models to 1808 hourly observations collected over four months. Anomaly detection was conducted using Isolation Forest (300 trees, contamination = 0.05) and One-Class Support Vector Machine (One-Class SVM) (RBF kernel, ν = 0.05, γ auto-scaled). Standardized six-dimensional feature vectors captured key environmental and energy-related variables. K-means clustering (k = 3) revealed three persistent operational states: Empty/Cool (42.6%), Experiment (37.6%), and Crowded (19.8%). Detected anomalies included CO 2 surges above 1800 ppm, TVOC concentrations exceeding 4000 ppb, and compound deviations in noise and temperature. The models demonstrated sensitivity to both abrupt and structural anomalies. Latent states were shown to correspond with occupancy patterns, experimental activities, and inactive system operation, offering interpretable environmental profiles. The methodology supports integration into adaptive heating, ventilation, and air conditioning (HVAC) frameworks, enabling real-time, label-free environmental management. Findings contribute to intelligent infrastructure development, particularly in resource-constrained laboratories, and advance progress toward sustainability targets in energy, health, and automation.

Keywords: smart laboratory; machine learning; event classification; indoor air quality (IAQ); TVOC; energy efficiency; sustainable buildings; context-aware systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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