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A performance assessment ontology for the environmental and energy management of buildings

handle: 10197/7330 , 10379/10918
Abstract Narrowing the performance deficit between design intent and the real-time environmental and energy performance of buildings is a complex and involved task, impacting on all building stakeholders. Buildings are designed, built and operated with increasingly complex technologies. Throughout their life-cycle, they produce vast quantities of data. However, many commercial buildings do not perform as originally intended. This paper presents a semantic web based approach to the performance gap problem, describing how heterogeneous building data sources can be transformed into semantically enriched information. A performance assessment ontology and performance framework (software tool) are introduced, which use this heterogeneous data as a service for a structured performance analysis. The demonstrator illustrates how heterogeneous data can be published semantically and then interpreted using a life-cycle performance framework approach.
- Ghent University Belgium
- University College Dublin Ireland
- University of Galway Ireland
- University of Galway Ireland
690, Ontology, Linked data, Performance management buildings, linked data, buildings, operation, semantic web, performance management, systems, ontology, optimization, Semantic web
690, Ontology, Linked data, Performance management buildings, linked data, buildings, operation, semantic web, performance management, systems, ontology, optimization, Semantic web
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).91 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
