Developing a Framework for Formulating Building Archetypes for Finnish Residential Buildings
Liyanage, Ruchira (2024)
2024
Master's Programme in Architecture
Rakennetun ympäristön tiedekunta - Faculty of Built Environment
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Hyväksymispäivämäärä
2024-11-15
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202410299585
https://urn.fi/URN:NBN:fi:tuni-202410299585
Tiivistelmä
The continuous anthropogenic alteration of Earth's environmental conditions underscores the requirement of resilient and energy-efficient human habitats. In Finland, much of the existing building infrastructure was originally designed for extreme cold. As climate conditions evolve, with more frequent and severe summer heatwaves and wind-driven rain, Finland’s building stock must be retrofitted to enhance energy performance and ensure climate resilience.
Residential buildings in Finland, which form a significant portion of the building stock, are particularly important due to their role in ensuring thermal comfort of residents, as well as their substantial energy consumption. Although they have evolved to meet regulatory requirements and occupant needs, these buildings currently require retrofitting and adaptation to reduce emissions, decrease energy consumption, and withstand the impacts of climate change. In this context, a bottom-up approach, using archetypes or representative building models, offers an effective means to streamline the simulation and analysis of large building stocks, facilitating the development of strategies for widespread energy and climate adaptations.
Current models of the climate resilience and energy efficiency of Finnish housing rely on small numbers of case study buildings, and it limits having a clear idea of the performance across the entire stock. This thesis aims to develop a framework for creating building archetypes specifically designed for Finnish residential buildings, with the flexibility to be adapted for other building types in the future.
The framework is grounded in existing literature. While there is data available for Finnish housing, this study proposes a novel approach that goes beyond utilizing traditional methods by employing a web scraping tool to acquire data from real estate websites, which helps gather more diverse and up to date information to complement traditional data sources. This study dives deeper into data processing and clustering methods to formulate well refined archetypes while highlighting the implications of building archetypes in real life situations.
While presenting a framework for the development of building archetypes, this study demonstrates the feasibility of using web scraping for archetype development. The framework outlines essential steps for development of archetypes for the purpose of conducting large-scale building simulations aimed at achieving climate resilience. As a secondary use these archetypes can be utilized to study the evolution of Finnish residential buildings.
Residential buildings in Finland, which form a significant portion of the building stock, are particularly important due to their role in ensuring thermal comfort of residents, as well as their substantial energy consumption. Although they have evolved to meet regulatory requirements and occupant needs, these buildings currently require retrofitting and adaptation to reduce emissions, decrease energy consumption, and withstand the impacts of climate change. In this context, a bottom-up approach, using archetypes or representative building models, offers an effective means to streamline the simulation and analysis of large building stocks, facilitating the development of strategies for widespread energy and climate adaptations.
Current models of the climate resilience and energy efficiency of Finnish housing rely on small numbers of case study buildings, and it limits having a clear idea of the performance across the entire stock. This thesis aims to develop a framework for creating building archetypes specifically designed for Finnish residential buildings, with the flexibility to be adapted for other building types in the future.
The framework is grounded in existing literature. While there is data available for Finnish housing, this study proposes a novel approach that goes beyond utilizing traditional methods by employing a web scraping tool to acquire data from real estate websites, which helps gather more diverse and up to date information to complement traditional data sources. This study dives deeper into data processing and clustering methods to formulate well refined archetypes while highlighting the implications of building archetypes in real life situations.
While presenting a framework for the development of building archetypes, this study demonstrates the feasibility of using web scraping for archetype development. The framework outlines essential steps for development of archetypes for the purpose of conducting large-scale building simulations aimed at achieving climate resilience. As a secondary use these archetypes can be utilized to study the evolution of Finnish residential buildings.