Enabling dynamic life cycle assessment of buildings with wireless sensor networks
This paper summarizes the goals and initial findings from a project whose aim is to create robust and practical life cycle assessment (LCA) tools to assess the performance of buildings. A central feature of this project is the concept of a dynamic LCA framework for buildings, which should include, b...
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Format: | Tagungsbericht |
Sprache: | eng |
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Zusammenfassung: | This paper summarizes the goals and initial findings from a project whose aim is to create robust and practical life cycle assessment (LCA) tools to assess the performance of buildings. A central feature of this project is the concept of a dynamic LCA framework for buildings, which should include, but is not limited to; considering temporal variations in internal and external conditions during a building's operating lifetime, and incorporating the ability to rapidly update the LCA results based on changes to the building's design or operation (dynamic scenario modeling). A life cycle assessment (LCA) framework is necessary to understand how buildings and their occupants use materials, water, and energy resources, and are affected by the building's internal environmental quality throughout the its lifetime. However, LCA is not commonly used in building industry practice. This disparity is hypothesized to be the result of several factors: the perceived and actual complexity of LCA's application; the lack of inclusion of internal building effects important to practitioners and users, such as indoor environmental quality (IEQ); and the lack of detailed information on the dynamics of the building's use phase. This paper describes the feasibility of deploying a real-time, wireless sensor network to generate a dynamic LCA for buildings. Using the data collected from a sensor network, projections of the environmental impact of a building's use phase can be validated or improved. Building systems that demonstrate a highly variable impact on the LCA results or diverge widely from current predictions can be selected for additional study, and choices regarding where to expend finite resources in sensor applications can be refined. |
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ISSN: | 1095-2020 2378-7260 |
DOI: | 10.1109/ISSST.2011.5936846 |