Development and Parametric Analysis of Hungary's Residential Building Stock Model on the Example of an Archetype Building

Abstract

This paper quantifies uncertainty and identifies the key drivers of simulated energy use in a representative archetype of the Hungarian residential building stock. It specifically examines the impact of standardized occupancy inputs compared to those based on surveys and stochastic methods. A DesignBuilder model of a typical detached house is parameterized using data from Energy Performance Certificates (EPCs) that detail the building's envelope and system characteristics. Additionally, occupant-related parameters such as setpoints, ventilation, domestic hot water (DHW), and internal gains are derived from a comprehensive national survey and relevant literature. To analyze uncertainty propagation and conduct a global sensitivity analysis, Latin Hypercube Sampling is applied across multiple scenarios: a typical meteorological year, two actual years, and a future climate-change scenario, with and without space cooling. Furthermore, an alternative scenario using a 2050 primary energy conversion factor is evaluated. The results indicate that the heating setpoint temperature is consistently the most influential factor for EPBD-based primary energy usage. Other significant contributors, depending on weather conditions and cooling assumptions, include ventilation rates, heating system efficiency, and parameters related to domestic hot water. Overheating hours are primarily affected by factors such as night ventilation, shading, and internal gains. The findings reveal that using standardized assumptions for occupancy can skew both heating and cooling outcomes. Additionally, assumptions regarding climate and primary energy factors can alter the relative significance of key parameters. The proposed workflow enhances the robustness of building-stock assessments and underscores the value of improving input data quality.