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Clustering as a simplification tool for the decision-making process on building stock renovation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rivallain, Mathieu Agapoff, Sergei Boisson, Pierre Foucquier, Aurélie Lee, Yun-Seok Vallée, Marne La |
| Copyright Year | 2020 |
| Abstract | Representing one of the largest energy consumer, the building sector is an important issue in terms of energy consumption and climate change mitigation. In this sense, many different actions of building energy savings have been started. Among them, retrofitting measures have been taken for different building scales from the single housing units up to the district or building stock. Finding retrofitting solutions for a single house is relatively easy, whereas the building stock dimension remains a complex issue. To facilitate the work of building stock owners and local public authorities, the developed method allows identifying groups of buildings and also at the scale of the building stock to be used as input for the retrofitting actions optimization. The proposed approach consists in clustering the buildings of a given stock according to their characteristics before refurbishment intrinsic variables as the geometry or the thermal properties – and the resulting variables as the total or heating energy consumption. Consequently, a methodology based on a clustering analysis has been implemented. It consists of successive clustering steps on a “decision space” containing the intrinsic building features and an “objective space” enclosing the energy performance. One of the main results is knowledge that the usual building classification based on climate, year of construction and type is not always sufficient and justified: compactness and height of should also be taken |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ibpsa.org/proceedings/BS2019/BS2019_210917.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
