Advanced data-driven methods for IOT sensor data and anomaly analysis in building environments
| dc.contributor.author | Wickramasinghe, Ashani | |
| dc.contributor.examiningcommittee | Yang, Po (Statistics) | |
| dc.contributor.examiningcommittee | Akcora, Cuneyt (Computer Science) | |
| dc.contributor.examiningcommittee | Feng, Cindy (Dalhousie University) | |
| dc.contributor.supervisor | Muthukumarana, Saman | |
| dc.date.accessioned | 2025-01-15T15:24:07Z | |
| dc.date.available | 2025-01-15T15:24:07Z | |
| dc.date.issued | 2024-12-25 | |
| dc.date.submitted | 2024-12-25T17:18:30Z | en_US |
| dc.date.submitted | 2025-01-14T23:57:29Z | en_US |
| dc.degree.discipline | Statistics | |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | |
| dc.description.abstract | In commercial buildings, maintaining and controlling the indoor environment while balancing thermal comfort, building health, and energy consumption can be challenging. This thesis focuses on developing statistical and machine learning methods to study the indoor environment of commercial buildings using IoT sensors to identify issues that could affect building health or tenant comfort. Controlling the temperature within an acceptable thermal comfort range is crucial. To address this, we developed and applied statistical methods to identify locations with similar environmental conditions, which helps optimize the HVAC (Heating, Ventilation, and Air Conditioning) system. In this study, we introduced a novel method for identifying time series clusters using community detection in network analysis. Additionally, buildings may have significantly high-temperature ‘hot spots’ and low-temperature ‘cold spots’. To identify these locations, we developed an improved hot spot analysis approach. We also proposed a model to detect locations with abnormal behaviors due to environmental issues. Finally, we developed a Bayesian predictive model using Bayesian networks by introducing a novel method for learning network structures to enhance prediction accuracy and estimate the probability of encountering abnormal locations in the future. | |
| dc.description.note | February 2025 | |
| dc.identifier.uri | http://hdl.handle.net/1993/38812 | |
| dc.language.iso | eng | |
| dc.subject | Anomaly Detection | |
| dc.subject | Bayesian Network | |
| dc.subject | Building Science | |
| dc.subject | Conditional Dependencies | |
| dc.subject | Hotspot Analysis | |
| dc.subject | Machine Learning | |
| dc.subject | Spatial Autocorrelation | |
| dc.subject | Structure Learning | |
| dc.subject | Time series clustering | |
| dc.title | Advanced data-driven methods for IOT sensor data and anomaly analysis in building environments | |
| local.subject.manitoba | no |