Surrogate models for predicting housing stock thermal comfort, and operational cost, energy or emissions have increasingly gained popularity due to the potential to identify solutions to accelerate and optimise construction. In addition to the potential opportunity to progress towards climate goals and increase resource efficiencies by identifying areas of interest, they are much less time consuming than brute-force simulations of millions of scenarios. Although they are popular due to their promise, analysis of scalability limits for surrogate model development methodologies have been left out. Without this analysis, assurance that these may be used over differing housing stock scales cannot be provided. This work uses black-box model interpretation as well as common predictive performance metrics to assess the scalability of the development methodology presented.The novelty of this work stems from its comparison of the performance of a development methodology through training a learning algorithm with a progressively varied dataset to illustrate its scalability. This validates the use of a single surrogate model to represent numerous bottom-up archetypes used to represent a housing stock.

中文翻译：

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表示住房存量的单一代理模型开发方法的局限性


用于预测住房存量、热舒适度和运营成本、能源或排放的替代模型越来越受欢迎，因为它有可能确定加速和优化施工的解决方案。除了通过确定感兴趣的领域来实现气候目标和提高资源效率的潜在机会外，它们比对数百万个情景的暴力模拟要耗时得多。尽管它们因其前景而广受欢迎，但对代理模型开发方法的可扩展性限制的分析已被遗漏。如果没有这种分析，就无法保证这些可以在不同的住房存量尺度上使用。这项工作使用黑盒模型解释以及常见的预测性能指标来评估所提出的开发方法的可扩展性。这项工作的新颖性在于它通过训练学习算法和逐渐变化的数据集来说明其可扩展性，从而比较了开发方法的性能。这验证了使用单个代理模型来表示用于表示住房存量的众多自下而上的原型。