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.

Occupant-centric controls (OCC) and operations have emerged as a key concept in shifting the focus from conventional building- (or system-) centric operations to a more occupant-centric approach. Despite the potential of OCC to meet occupants’ demands and bridge buildings’ energy performance gap, its implementation in real-world settings has been limited. In addition, there is a lack of standardization in methodologies and terms to facilitate meaningful comparisons among case studies. Therefore, this paper aims to present a repository of OCC case studies, offering a platform for standardization and presenting key information about practical implementations of these strategies in real-world scenarios. To accomplish this, descriptors, terms, and concepts about OCC case studies were discerned through a literature review. These elements were systematically integrated into a structured survey to capture comprehensive information on OCC field study implementations. This paper provides an overview of the survey structure and insights into the current dataset, which comprises a total of 58 OCC case studies. By publishing the case study repository, we intend to establish standard categories for OCC strategies and to offer researchers and practitioners a database through which they can understand trends and possibilities for implementing OCC strategies.

Climate change impacts the entire planet, and its effects are particularly evident in urban areas. Northern cities in Brazil experience a hot and humid climate, which poses a challenge to achieving high levels of thermal performance in housing developments. This challenge is amplified by the fact that most residents do not have access to air conditioning systems, making it difficult to mitigate the heat. Current technologies have the potential to confront this critical situation by diagnosing thermal performance and implementing optimized strategies for modeling multiple climatic scales, including the city, neighborhood, and indoor environment. Therefore, this study aims to fill a research gap by utilizing simulations to predict and optimize the thermal performance of naturally ventilated social housing in hot and humid equatorial climates, while considering the effects of climate change. Adaptive modeling principles were applied, fostering synergy among the meso, local, and microclimatic scales through a unidirectional simulation. The results revealed that the region experiencing the highest real estate growth has witnessed a significant increase in temperature over the years. The comparison between historical and future climate files confirmed predictions of climate change in a pessimistic scenario, particularly regarding temperature and relative humidity indicators. When climate files adjusted for future climate conditions were used, it was discovered that passive building design strategies had a stronger impact on the microclimate compared to heat island mitigation strategies. This impact led to better building thermal performance. However, at the building scale, thermal performance is highly influenced by climate change and could be reduced by up to 11% (in 2020) and 39% (in 2050).

Downward long-wave radiation is an important parameter for building simulation because it strongly influences the thermal balance of the external surface. Brazil has several distinct climatic regions, and little formal work has been done with observed downward long-wave radiation to build thermal energy balancing due to a lack of continuous data. In this regard, selecting a model and adjusting local parameters is fundamental for reducing the uncertainty of the computational simulation process. In the present study, we use data from four different latitude sites to adjust the parameters of four models integrated into the EnergyPlus simulation program. The results obtained in the analysis indicated that with an appropriate adjustment of local parameters, all models achieved good predictive performance and provided lower errors values than previous versions, when using versions incorporated into the EnergyPlus, and when using original versions. In addition, it is recommended to use the Berdahl and Martin’s [10] model adjusted by this study in the building energy simulations carried out in Brazil, since it obtained the lowest rRMSE values, between 2.2% and 2.5%, while the standard model of EnergyPlus varies between 5.1% and 10.5%. After validating the adjustments and considering a single-family house of 43.25 m², it was concluded that without proper adjustment, the models incorporated into the EnergyPlus, overestimate the total annual thermal load by 8% to 37% when compared to the model the proposed in the present work.