The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

减少建筑环境和建筑行业的能源消耗和环境影响对于全球范围的可持续性至关重要。我们正在努力提高对建筑环境中的资源效率和遵循循环经济原则的创新业务增长的承诺。变化的概念化是能源和可持续性转型研究的重要组成部分，旨在实现与社会功能兼容的根本性转变。在此框架中，必须从整个生命周期的角度考虑建筑物的性能，因为建筑物是长期资产。从生命周期的角度来看，为了适当的可比性和决策，必须同时考虑运营和体现的能源和碳排放。在建筑环境中对产品和实践的可持续性评估的应用本身是一个至关重要且值得商issue的问题。因此，能源消耗数据的测量，处理和报告方式必须逐步标准化，以确保多种规模（从单个建筑物到建筑存量）和分析级别（从各个组成部分）的方法的透明性和一致性。直至系统），理想地补充了在能源研究中使用开放科学原理的正在进行的研究计划。在本文中，我们通过在建筑物中使用基于回归的方法来分析将设计阶段与运行阶段的能源性能分析联系起来的主题，突出了建筑能源建模数据的分层性质。这项研究的目的是回顾当前的技术水平，以便将未来的工作定向到从设计到操作的集成数据分析工作流。从这个意义上讲，我们展示了如何使用数据分析技术来评估技术和人为因素的影响。最后，我们指出回归模型的近似物理解释如何帮助开发数据驱动的模型，从而可以提高从反馈中学习和在多个级别重建建筑存量数据的可能性。