Using archetypes for building stock energy modeling by applying Felicity and AssessCity softwares

Building Stock Energy Modeling (BSEM) is a critical methodology in the pursuit of sustainable urban development and effective energy management. As urban areas expand, buildings account for a significant portion of global energy consumption and carbon emissions. To address these challenges, the European Union has implemented directives aimed at optimizing energy performance in buildings. One critical approach to achieving this goal is through Building Stock Energy Modeling (BSEM), which facilitates data-driven strategies for energy efficiency improvements. This research focuses on developing and evaluating the performance and accuracy of BSEM tools: AssessCity, and Felicity. The study leverages standardized datasets from the TIMEPAC and TABULA projects, which provide comprehensive data on building parameters such as occupancy, envelope characteristics, energy systems, and environmental conditions. This data was meticulously organized into detailed datasheets, supplemented with Italian standards and legislation to ensure contextual relevance and accuracy. Seven case study buildings from the Piemonte region of Italy were selected, with real energy audit data serving as benchmarks to validate the simulation models. Each building was modeled using DesignBuilder, AssessCity, and Felicity to simulate energy performance. The models were evaluated based on key performance indicators, including Root Mean Square Error (RMSE), Coefficient of Variation of RMSE (CVRMSE), and Mean Bias Error (MBE), to assess their predictive accuracy. In addition to technical performance, the study examines the limitations of the current modeling approaches, such as data variability, the generalization of building archetypes, and the lack of occupant behavior integration. Recommendations for future research include the incorporation of real-time energy consumption data, the application of machine learning algorithms to enhance predictive accuracy, and the exploration of occupant behavior's impact on energy performance. This work contributes to the development of more reliable and efficient energy modeling practices, providing valuable insights for urban planners, policymakers, and energy consultants engaged in sustainable development projects.