Impact of data availability on building energy performance simulation: A case study of a Dutch terraced house

Estimating building energy performance remains a challenge due to uncertainties in user behaviour and model simplifications. This study investigates how data availability influences discrepancies between measured and simulated performance, focussing on occupant behaviour uncertainties and modelling assumptions. A comprehensive analysis distinguishes different types of uncertainties and errors in energy modelling. A calibration study is conducted using an existing residential building in the Netherlands. The model is calibrated by progressively increasing data availability levels, incorporating high-quality measurements of environmental conditions, system operations, and occupant behaviour inputs across multiple scenarios. The results show that the integration of detailed occupant data significantly improves the accuracy of the model, reducing the heating usage deviations from 100% (the base case scenario established on the default assumptions) to approximately 5%. Post-calibration evaluations confirm that the monthly mean biased error and the coefficient of variation of the root mean squared error for heating and indoor temperature predictions align with ASHRAE Guideline 14 thresholds. However, refinements in ventilation modelling and behavioural data, such as radiator usage, are needed for further accuracy. This study quantifies the impact of data availability on simulation reliability and provides practical guidance for selecting appropriate abstraction levels based on available data quality rather than simply maximizing model complexity. Practical Application This study presents practical implications for stakeholders. The results guide the modellers, emphasising transparency in assumptions, simplifications, input parameters, and modelling methods, while considering error margins to improve decision-making accuracy. Robust calibration practices are vital to improve the accuracy of the model, communicate uncertainties, and account for potential errors. For policymakers, the findings stress the need to establish clear data collection standards for new buildings and promote best practices that ensure transparency in modelling assumptions. These insights are crucial for strengthening confidence in building models, improving their reliability, and supporting well-informed decisions across diverse contexts.