Integration of geospatial and environmental data to support building renovation

Building renovation can present more complexities compared to constructing new buildings due to the necessity of dealing with existing conditions. This is not only limited to the building as-is condition but also encompasses the urban, environmental, and societal context of the building. A key goal of building renovation is to improve the performance of the building and comfort of inhabitants. By taking into account the complexity of the urban context and integrating it in the building modeling process, it is possible to achieve more accurate building simulations, which leads to a more realistic assessment of the building’s performance and comfort. A prerequisite to determining the important external features is having a thorough understanding of the related concepts in the external domain. The primary objective of this dissertation is to develop such a knowledge framework to be used as a frame of reference for the experts and engineers involved in renovation projects. Building within an urban context is surrounded and influenced by various urban features, including neighboring buildings, roads, vegetation, and water bodies. These features are represented and characterized within the geospatial domain. Various data standards and schemes have been developed to represent this domain. However, these representations are not directly applicable to building renovation since they lack a focus on the required or beneficial concepts for this task and fail in incorporating expert knowledge in developing the model. Hence, this dissertation proposes a knowledge framework in the form of an ontology to represent the required and beneficial concepts of geospatial domain to support building renovation activities. The proposed knowledge framework is built upon the knowledge captured from previous studies that implicitly mention the effect of such datasets in building renovation. Also expert knowledge is integrated through workshops and brainstorming, and the concepts are identified based on specific tasks and for particular use cases within renovation projects. The applicability of this knowledge framework is demonstrated for site planning use case of a residential building under renovation in Berlin, Germany. The development of this knowledge framework showed that selection of various weather datasets and the Inter-Building Effect (IBE) are key factors in building performance. To present the sensitivity of building performance to such factors and to determine the extent of their impact, the dissertation explores various alternatives through analytical studies and gain insights into the complex interplay between the building and its environment. This involves systematically analyzing different configurations, and input variables to assess their influence on the building's behavior and performance. This approach facilitates a more comprehensive and realistic assessment of building performance, leading to enhanced design decisions and improved occupant comfort. In this regard, in the second stage of the research, to explore the sensitivity of building performance to the selection of weather data, various typical-year weather datasets retrieved from different sources and generated from different periods and methodologies are applied in the energy simulation of three residential buildings in Europe. The research reflects on the range of disparities caused by these alternatives. Based on that, it highlights the effect of such parameters and calls on decision-makers in renovation projects to meticulously investigate the influence of their weather data selection. The research also highlights the impact of policymaking. It suggests that standardization organizations should develop new approaches for developing weather datasets that represent long-term climate conditions more realistically. The last stage of the research focuses on the sensitivity of building performance to IBE, through studying the shading effect of surrounding buildings on the building performance of renovation projects. The research expands simplified urban typologies by generating randomly selected heights and distances of surrounding buildings in a nine-block network. Following that, the impact of such typologies are investgated on the thermal demand and illuminance level of various residential buildings in Europe. The research reflects on the range of disparities caused by each urban layout and emphasizes the effect of such input variables on building thermal and visual performance. The study also highlights the need to thoroughly investigate the impact of such data on the decision-making process of renovation scenarios and the possibility of integrating specific materials, shading devices, and so on in the renovation workflow. It also proposes a workflow for IBE integration to the renovation roadmaps. Studying the weather data selection and IBE highlights that renovation projects should consider the future possible alternatives of the built environment to compensate for the renovation costs. Therefore, the main contributions of this research can be summarized as follows: 1) a knowledge framework in the form of an ontology to represent concepts in the geospatial domain to support energy efficiency of building renovation; 2) two explorative analyses to investigate the sensitivity of building performance to a) various weather datasets, b) various complex urban typologies. The dissertation also discusses the role of policymaking and standardization in effectively including such variables in renovation projects.

Author dc:creator

• Daneshfar, Maryam

Advisor dc:contributor.advisor

• Hartmann, Timo

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Identifier URI

https://doi.org/10.14279/depositonce-20963

OAI identifier oai:identifier

oai:depositonce.tu-berlin.de:11303/22162