Urban residential building stock synthetic datasets for building energy performance analysis.

The urban building stock dataset consists of synthetic input and output data for the energy simulation of one million buildings. The dataset consists of four different residential types, namely: terraced, detached, semi-detached, and bungalow. Constructing this buildings dataset requires conversion, categorization, extraction, and analytical processes. The dataset (in .csv) format comprises 19 input parameters, including advanced features such as HVAC system parameters, building fabric (walls, roofs, floors, door, and windows) U-values, and renewable system parameters. The primary output parameter in the dataset is Energy Use Intensity (EUI in kWh/(m2*year)), along with Energy Performance Certificate (EPC) labels categorized on an A to G rating scale. Additionally, the dataset contains end-use demand output parameters for heating and lighting, which are crucial output parameters. jEPlus, a parametric tool, is coupled with EnergyPlus and DesignBuilder templates to facilitate physics-based parametric simulations for generating the dataset. The dataset can be a valuable resource for researchers, practitioners, and policymakers seeking to enhance sustainability and efficiency in urban building environments. Furthermore, dataset holds immense potential for future research in the field of building energy analysis and modeling.

Zone-wise occupancy schedules developed using Time Use Survey data for building energy performance simulations.

The occupancy profile dataset presented in this study leverages publicly available UK Time Use Survey (TUS) 2014-15 data to evaluate the impact of occupancy on energy consumption at various spatial and temporal scales using multi-scale archetypes. Constructing this occupancy dataset includes conversion, categorisation, extraction and analysis processes. The resulting dataset (in .csv) format represents realistic day-wise zone-level occupancy availability schedules that account for the effect of the type of dwelling, the number of occupants, the month of the year and the day of the week. A total of 5,376 occupancy profiles were extracted, representing a large number of dwellings. These profiles demonstrate the realistic behaviour of occupants' availability in dwellings. These profiles allow us to gain valuable insights into the energy usage patterns in dwellings based on the realistic behaviour of occupants, leading to more accurate and context-specific energy assessments.