The multi-domain comfort theory investigates human-environmental perception and comfort by accounting for people's simultaneous exposure to various stimuli from different physical domains. Multi-domain studies describe human reactions to environmental conditions, including indoor occupants' behaviour and comfort. Building simulation is essential to analyse Indoor Environmental Quality (IEQ) and energy consumption in buildings. Introducing multi-domain comfort theories in building simulation practices could improve reliability. A systematic literature review investigated the approaches adopted in multi-domain building simulation during the last decade. The simulation studies discussed herein combine two or more domains related to IEQ in buildings, indicating the state of the art, limitations, and potential trends. This review showed that multi-domain simulation comprising all the IEQ domains is still missing due to its complexity and the lack of standards for multi-domain comfort. Simulation studies mostly involved two domains - thermal and air quality or thermal and visual. The most common engines and software combinations were presented, and related interoperability issues were discussed. The most common inputs and outputs for each domain were described to identify common ground where to start building up an efficient multi-domain simulation framework. The role of the quality report was also addressed, pointing out that the current validation procedures are incipient. Advancing multi-domain simulation knowledge concurrently with understanding multi-domain comfort growth would benefit researchers and practitioners. Therefore, multi-domain simulations can become a powerful tool to guide occupant-centric building design and operation.

The energy sector significantly impacts the environment, with energy production contributing to greenhouse gases emissions and climate change. In Brazil, buildings account for a substantial portion of energy consumption, making energy efficiency essential for sustainable development. Building simulation is an efficient way to provide valuable insights into the thermal performance of buildings, but it requires expertise, time, and computational resources. To overcome these simulation constraints, metamodeling has emerged as an easy-to-use and fast-response alternative to analyse the thermal performance of buildings. This study focuses on developing a metamodel to predict cooling thermal loads in Brazil's commercial, services, and public buildings, supporting the country's building energy efficiency labelling program. It is expected from the metamodel a high capacity to reproduce the variability of climates, contexts, and heterogeneity of buildings from a country-level perspective. A parametric sampling process was used to develop a comprehensive simulated database considering several variations of building-related, occupancy patterns, and weather parameters. The metamodel was trained, validated and tested using optimisation techniques and an artificial neural network. Afterwards, it was compared with actual models, considering different typologies and climates. While the metamodel demonstrates high accuracy and generalisation, limitations were found regarding its application in warmer temperatures and complex building shapes. Further refinement is needed to improve its applicability and reliability in real-world scenarios. The proposed metamodel offers a practical and widely applicable tool for supporting energy code compliance and energy efficiency assessment in buildings.