{"id":116686,"date":"2026-09-11T15:11:47","date_gmt":"2026-09-11T13:11:47","guid":{"rendered":"https:\/\/nilu.gnist.dev\/publikasjoner\/predicting-the-students-perceptions-of-multi-domain-environmental-factors-in-a-norwegian-school-building-machine-learning-approach\/"},"modified":"2026-09-11T15:11:47","modified_gmt":"2026-09-11T13:11:47","slug":"predicting-the-students-perceptions-of-multi-domain-environmental-factors-in-a-norwegian-school-building-machine-learning-approach","status":"publish","type":"nva_publication","link":"https:\/\/nilu.gnist.dev\/en\/publications\/predicting-the-students-perceptions-of-multi-domain-environmental-factors-in-a-norwegian-school-building-machine-learning-approach\/","title":{"rendered":"Predicting the student&#8217;s perceptions of multi-domain environmental factors in a Norwegian school building: Machine learning approach"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Poor Indoor Environmental Quality (IEQ) in schools significantly impacts students\u2019 well-being, learning capabilities, and health. Perceived dissatisfaction rates (PD%) among students often remain high, even when indoor environmental variables appear well-controlled. This study aims to predict perceived dissatisfaction rates (PD%) across multi-domain environmental factors\u2014thermal, acoustic, visual, and indoor air quality (IAQ)\u2014using machine learning (ML) models. The research integrates sensor-based environmental measurements, outdoor weather data, building parameters, and 1437 student survey responses collected from three classrooms in a Norwegian school across multiple seasons. Statistical tests were used to pre-select relevant input variables, followed by the development and evaluation of multiple ML algorithms. Among the tested ML models, Random Forest (RF) demonstrated the highest predictive accuracy for PD%, outperforming multi-linear regression (MLR) and decision trees (DT), with R\u00b2 values up to 0.91 for overall IEQ dissatisfaction (PDIEQ%). SHAP analysis revealed key predictors: CO\u2082 levels, VOCs, humidity, temperature, solar radiation, and room window orientation. IAQ, thermal comfort, and acoustic environment were the most influential factors affecting students&#8217; perceived well-being. Despite limitations as implementation in building level scale, the study demonstrates the feasibility of deploying predictive ML models under real-world constraints for improving IEQ monitoring system. The findings support practical strategies for adaptive indoor environmental management, particularly in educational settings, and provide a replicable framework for future research. Future research can expand to other climates, buildings, measurements, occupant levels, and ML training optimization.<\/p>\n","protected":false},"template":"","meta":{"_acf_changed":false,"_searchwp_excluded":"","_id":"","_status":"PUBLISHED","_sync_date":"2026-09-11 14:13:32","footnotes":""},"nva_tax_category":[1099],"class_list":["post-116686","nva_publication","type-nva_publication","status-publish","hentry","nva_tax_category-scientific-journal-publication"],"acf":[],"_links":{"self":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/116686","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication"}],"about":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/types\/nva_publication"}],"version-history":[{"count":0,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/116686\/revisions"}],"wp:attachment":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/media?parent=116686"}],"wp:term":[{"taxonomy":"nva_tax_category","embeddable":true,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_tax_category?post=116686"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}