A Multi-criteria and Multi-domain Model to Support the Comprehensive Assessment of Building Performance

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Abstract

Buildings can play a significant role in reducing energy consumptions and related emissions. However, the renovation process towards better-performing buildings is slowed down also by non-technical barriers, like the lack of accounting for indirect impacts of building renovation. Benefits of building retrofit that are not yet reflected by their market value, e.g., increase in people comfort and well-being, reduction of environmental impacts, must be quantified to make them tangible and, thus, to become leavers towards new habits and investment decisions. Thanks to the spreading of smart sensing in the building sector and the increasing availability of monitored data coming from indoor spaces, the control of such aspects in building design and management practices will become more and more feasible. The paper aims at proposing a multi-criteria and multi-domain (energy, environmental, financial, and socio-economic) methodology for building performance assessment, exploiting monitored data. The set of the evaluation criteria per each domain, namely the Key Performance Indicators (KPIs), and the parameters needed for their quantification represent the main results of this research. Moreover, the research contributes to identifying an evaluation framework based on the Multi Attribute Value Theory, built on the standardization of the various indicators through so-called value functions. This integrated approach enables the translation of monitored data into useful and easy-to-understand metrics to support building design and management. Future works will include the translation of such approach into informative services and the deepening of the relative weights of KPIs and domain of impacts in the evaluation schema.

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APA

Vergerio, G., Cavana, G., Dell’Anna, F., Becchio, C., Viazzo, S., & Bottero, M. (2022). A Multi-criteria and Multi-domain Model to Support the Comprehensive Assessment of Building Performance. In Lecture Notes in Networks and Systems (Vol. 482 LNNS, pp. 1752–1761). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-06825-6_169

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