Operational performance benchmarking for commercial buildings by using text analytics on work order logs and tenant survey data
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- Huseyn B. Gunay (Co-author)
- Scott Bucking (Co-author)
- Abstract
Operational performance metrics are necessary to detect anomalous floors, equipment, and work-order categories in large commercial buildings. To this end, this thesis presents a method to extract operational performance metrics from computerized maintenance management systems (CMMS) and text-based tenant surveys. The method was demonstrated by using work-order logs and text-based tenant survey data gathered from four large commercial buildings in Ottawa, Canada. The analysis of CMMS data highlights the potential of decision trees, Sankey diagrams and association node networks to effectively visualize anomalies in building complaint patterns. Investigation of the text-based tenant surveys using established text analytics algorithms reveals that classifiers are more accurate for sentiment analysis than lexicon-based methods while both association rule mining and topic modelling algorithms successfully uncover key operational insights. Finally, a software tool mock-up was developed that combines the most impactful elements from previous work for building owners to visualize complaint patterns and maintenance workflows.
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Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2020
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dutta-operationalperformancebenchmarkingforcommercial.pdf | 2023-05-05 | Public | Download |