The Open Data Potential for the Geospatial Characterisation of Building Stock on an Urban Scale: Methodology and Implementation in a Case Study †.

Energy renovation in buildings is one of the major challenges for the decarbonisation of the building stock. To effectively prioritise decision making regarding the adoption of the most efficient solutions and strategies, it is imperative to develop agile methods to determine the energy performance of buildings on an urban scale, in order to evaluate the impact of these improvements. In this regard, the data collection for feeding building energy models plays a key role in the accuracy and reliability of this issue, and the significant increase in recent years of available data from open data sources offers great potential in this respect. Thus, this study focuses on proposing a systematised and automated method for obtaining information from open data sources so as to obtain the most relevant geometric and thermal characteristics of residential buildings on an urban scale. The criteria for selecting the parameters to be obtained are based on their potential use as input data in different energy demand models aimed at assessing the energy performance of the building stock in a given area and, eventually, to evaluate the potential for improvement and the mitigation of different strategies. Geometric characterisation relies on obtaining and processing open data from cadastres to extract envelope surfaces categorised by orientation through QGIS (Free and Open Source Geographic Information System). For thermal characterisation, an automated process assigns different parameter-based information obtained from cadastral data, such as the year of construction. Finally, the applicability of the method is demonstrated through its implementation in the case study of Bilbao (Spain). The obtained results show that, although additional data should be collected when a detailed analysis of a building or building cluster has to be carried out, the existing open data can provide a first approximation, providing a first global view of the building stock in a region. It demonstrates the usability of the proposed method as an effective way to obtain and process these relevant data. [ABSTRACT FROM AUTHOR]