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

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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.