Urban energy consumption is expected to continuously increase alongside rapid urbanization. The building sector represents a key area for curbing the consumption trend and reducing energy-related emissions by adopting energy efficiency strategies. Building age acts as a proxy for building insulation properties and is an important parameter for energy models that facilitate decision making. The present study explores the potential of predicting residential building age at a large geographical scale from open spatial data sources in eight municipalities in the German federal state of North-Rhine Westphalia. The proposed framework combines building attributes with street and block metrics as classification features in a Random Forest model. Results show that the addition of urban fabric metrics improves the accuracy of building age prediction in specific training scenarios. Furthermore, the findings highlight the way in which the spatial disposition of training and test samples influences classification accuracy. Additionally, the paper investigates the impact of age misclassification on residential building heat demand estimation. The age classification model leads to reasonable errors in energy estimates, in various scenarios of training, which suggests that the proposed method is a promising addition to the urban energy modelling toolkit.

随着快速的城市化，预计城市能源消耗将持续增加。建筑部门是通过采用能效战略来抑制消费趋势和减少与能源有关的排放的关键领域。房屋使用年限可作为建筑物隔热性能的代名词，并且是有助于决策的能源模型的重要参数。本研究探索了从德国北莱茵-威斯特法伦州的八个城市的开放空间数据源中，在较大的地理范围内预测住宅建筑年龄的潜力。所提出的框架将建筑属性与街道和街区指标结合在一起，作为随机森林模型中的分类特征。结果表明，在特定的训练场景中，添加城市结构指标可以提高预测建筑年龄的准确性。此外，研究结果突出了训练和测试样本的空间布置影响分类准确性的方式。此外，本文还研究了年龄错误分类对住宅建筑供热需求估算的影响。在各种培训方案中，年龄分类模型会导致能源估算出现合理误差，这表明所提出的方法是对城市能源建模工具包的有前途的补充。本文研究了年龄错误分类对住宅建筑供热需求估算的影响。在各种培训方案中，年龄分类模型会导致能源估算出现合理误差，这表明所提出的方法是对城市能源建模工具包的有前途的补充。本文研究了年龄错误分类对住宅建筑供热需求估算的影响。在各种培训方案中，年龄分类模型会导致能源估算出现合理误差，这表明所提出的方法是对城市能源建模工具包的有前途的补充。