Application of clustering for the development of retrofit strategies for large building stocks

作者：Geyer Philipp; Schluter Arno; Cisar Sasha

来源：Advanced Engineering Informatics, 2017, 31: 32-47.

DOI：10.1016/j.aei.2016.02.001

• Algorithmic clustering
• Building retrofitting
• Strategic building stock management
• Data mining

In order to reduce energy consumption and emissions from the built environment, it is vital to transform the existing building stock and develop retrofit strategies to achieve energy efficiency and building integrated renewable energy supply. Compared to developing cost-optimal retrofit strategies for one building, the development of strategies for 100 to up to 10,000 buildings remains a major challenge. This paper presents a method to cluster buildings based on their sensitivity to different retrofit measures, focusing on the cost-effectiveness. Derived from algorithmic clustering and combined with time and cost data, a tailored development of retrofit strategies for large building stocks becomes possible. Improved identification of retrofit measures and strategies, in contrast to the conventional classification based on building type and age, is demonstrated. The method is illustrated, using the data from the case study project 'Zernez Energia 2020', which aims to achieve carbon neutrality of a Swiss alpine village.

• 出版日期2017-1