Engineering-based reduced-order modelling of districts in the context of heuristic life cycle assessment

启发式生命周期评估背景下基于工程的地区降阶建模

• 批准号: 531801923
• 负责人: Professor Dr.-Ing. Christoph van Treeck
• 金额: --
• 依托单位: Lehrstuhl für Energieeffizientes Bauen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/531801923?language=en
• 关键词: Engineering; based; reduced; order; modelling

The methodology of life cycle assessment (LCA) has been gaining importance rapidly due to ambitious global and national climate protection goals. In the building sector, district-scale estimations of greenhouse gas emissions and further pollutants are necessary for the broad determination of decarbonisation measures. Yet, the consideration of all district life cycle phases implies a high granularity and sensitivity of input and output parameters. This is exacerbated by a lack of life cycle inventory (LCI) data and high computational effort for the large-scale determination of building energy demands. To tackle these issues, archetypes and low-order building models have been developed. The former is intended to cluster buildings with a high degree of similarity, while the latter is meant to reduce the overall amount of necessary input parameters. However, the static nature of archetypes and inherent volatility of input parameters along all life cycle phases emphasise the necessity of a model that can be adapted continuously. Furthermore, the broad district-scale determination of pollutants requires a generic approach for the consideration of residential as well as non-residential buildings in new construction and refurbishment. This approach should make it possible to estimate emissions by aid of a small range of input parameters to reduce the computational effort. Thus, the aim of this project is to develop a generic top-down urban modelling approach for the heuristic prediction of environmental indicators. A broad bandwidth of endogenous and exogenous data shall be used to explore statistically significant correlations currently unknown between modelling and simulation input on the one hand, and impact assessment output on the other hand. For data interpolation and enrichment, synthetisation tools will be employed. Data pre-processing and data mining will be used to prepare the collected data as training and testing sets for different machine learning methods. These methods will be evaluated in terms of accuracy, runtime and data demand to identify the best-performing modelling approaches. To increase the general availability of highly detailed building models for LCA purposes, pre-existing synthetisation tools for interpolation, transformation and enrichment of CityGML building models shall be advanced by the use of the collected data. The developed machine learning models will be made publicly available for the continuous adaptation and to produce more accurate district LCA results than a static archetype approach.

由于全球和国家气候保护目标的雄心勃勃，生命周期评估（LCA）方法迅速变得越来越重要。在建筑领域，对温室气体排放和其他污染物的地区规模估计对于广泛确定脱碳措施是必要的。然而，考虑所有地区生命周期阶段意味着输入和输出参数的高粒度和敏感性。由于缺乏生命周期清单（LCI）数据和大规模确定建筑能源需求的高计算工作量，这种情况更加严重。为了解决这些问题，开发了原型和低阶建筑模型。前者旨在聚集具有高度相似性的建筑，而后者旨在减少必要输入参数的总量。然而，原型的静态性质和所有生命周期阶段输入参数的固有波动性强调了可以持续适应的模型的必要性。此外，在广泛的地区范围内确定污染物需要一种通用的方法来考虑新建和翻新中的住宅和非住宅建筑物。这种方法可以通过一个小范围的输入参数来估计排放量，从而减少计算工作量。因此，该项目的目的是开发一种通用的自上而下的城市建模方法，用于启发式预测环境指标。宽带宽的内源性和外源性数据应使用，以探索建模和模拟输入与影响评估输出之间目前未知的统计显著相关性。对于数据插值和丰富，将使用合成工具。数据预处理和数据挖掘将用于准备收集的数据作为不同机器学习方法的训练和测试集。这些方法将在准确性，运行时间和数据需求方面进行评估，以确定最佳表现的建模方法。为了提高高度详细的建筑模型的普遍可用性，将利用收集到的数据，进一步完善现有的综合工具，对CityGML建筑模型进行插值、转换和丰富。开发的机器学习模型将公开用于持续适应，并产生比静态原型方法更准确的区域LCA结果。