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.

With climate change leading to more frequent, more intense, and longer durations of extreme weather events such as heat waves and cold snaps, it is essential to maintain safe indoor environmental conditions for occupants during such events, which may coincide with, or even cause, power outages that expose residents to health risks. Analyzing the impacts of extreme weather events on the thermal resilience of buildings can help stakeholders (including occupants) understand the risk and inform them about mitigation and adaptation actions. Moreover, analyzing the technological, social and policy dimensions of thermal resilience is critical for climate-proofing buildings. This paper presents 10 questions that highlight the most important issues regarding the thermal resilience of buildings for occupants in the face of climate change. The proposed questions and answers aim to provide insights into current and future building thermal resilience research and applications, and more importantly to inspire new significant questions in the field.

This study aims to establish optimal cases for energy-efficient social housing projects in Brazil, considering a life cycle cost-benefit analysis. Energy efficiency measures for the building envelope were evaluated in representative single-family and multifamily typology projects for the lower-income sector, considering thermal autonomy, energy consumption and cost indicators. The measures estimated in terms of macrocomponents allowed for comparative evaluation and association with costs. Cases were simulated in EnergyPlus to obtain the building’s expected operational consumption. With the macrocomponents and simulation data, Python routines were used to compute results for each proposed combination. Optimal cases that present high thermal performance in the national standard (NBR 15575) with cost-effectiveness, offering guidelines for projects in the 8 Brazilian bioclimatic zones, were established. Results presented in more detail for bioclimatic zone 8 showed optimal cases, with the increase in the percentage of hours of thermal autonomy reaching 65%–71%, whereas that of a typical building was 30%, while life cycle cost related to the envelope showed reductions of up to 22%, even with higher initial costs. This study served as a basis for the new Brazilian policy proposed by the National Housing Office (SNH) to develop new social housing projects.

Occupant-Centric Control and Operation (OCC) represents a transformative approach to building management, integrating sensing of indoor environmental quality, occupant presence, and occupant-building interactions. These data are then utilized to optimize both operational efficiency and occupant comfort. This paper summarizes the findings from the IEA-EBC Annex 79 research program's subtask on real world implementations of OCC during the past 5 years. First, in Q1 and Q2, we provide a definition and categorization of OCC. Q3 addresses the role of building operators for OCC, while Q4 describes the implications for designers. Then, Q5 and Q6 discuss the role and possibilities of OCC for load flexibility, and for pandemic induced paradigm shifts in the built environment, respectively. In Q7, we provide a taxonomy and selection process of OCC, while Q8 details real world implementation case studies. Finally, Q9 explains the limits of OCC, and Q10 provides a vision for future research opportunities. Our findings offer valuable insights for researchers, practitioners, and policy makers, contributing to the ongoing discourse on the future of building operations management.

Technologies based on phase change materials (PCMs) are promising solutions to reduce energy consumption in buildings and related greenhouse gas emissions. However, the performance of passive PCMs in buildings is highly dependent on the melting temperatures employed, as well as the climate where the building is located. Therefore, the present contribution describes an optimization-based method to design passive latent energy storage in buildings by using PCMs with different melting temperatures. To achieve this goal, a multi-objective genetic algorithm is coupled with the building energy models developed in EnergyPlus to find the best trade-off between annual heating and cooling loads. A small office is chosen as a case study to evaluate the energy performance of the buildings incorporating the proposed PCM approach. Three different PCM layers are added to the ceilings and the external and internal walls of the building, and their parametric models are developed in EnergyPlus to optimize the melting temperature and thickness of each PCM layer simultaneously. Moreover, a method to select climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe) is proposed and applied, resulting in eight well-representative locations. An optimization-based design is carried out for each selected location and the performances of the optimized building designs are systematically compared to the ones of the baseline models. The optimization results achieved show that regardless of the climate zone analyzed, using several PCMs with different melting temperatures instead of a single one, is preferred. Moreover, the best performance of PCMs is attained in climate zones where both the heating and cooling loads are present. Thus, the highest saving regarding the annual total loads of 11.7% is achieved in zone 5A (Cold), while the lowest one of 2.3% is obtained in zone 1B (Very hot).

Demand for artificial cooling in buildings is increasing worldwide, and it is expected to continue to grow in the upcoming decades. To mitigate energy use, the adoption of passive cooling strategies such as natural ventilation is a solution. In this study, a metamodel is developed to estimate thermal performance in naturally ventilated cellular offices in Brazil. Metamodels help to overcome the complexity of building simulation, although some caveats should be considered when developing such models. Simulation parameters were based on a database of cellular office buildings from São Paulo. The output of the simulations was the fraction of hours with operative temperatures above ASHRAE’s Standard 55 adaptive model. Two different modeling approaches were analyzed, a single-zone and a multi-zone. Sensitivity analyses were conducted to identify what parameters are the most influential on simulation results, and what parameters compromise the accuracy of the single-zone approach. Window opening factor, walls’ transmittance and the condition of exposure of walls and windows were the most influential parameters on the simulations. Although the single-zone approach is simpler, it does not consider heat transfer between different offices adequately. From the two modeling approaches, two metamodels were developed. The single-zone metamodel had a more accurate performance when validated on single-zone simulations. However, the errors related to this modeling approach compromises the performance when compared to multi-zone simulations. It is concluded that it is important to understand the limitations and accuracy of a surrogate model before applying it, and that such tool could be helpful for building designers.

Scientific literature about building occupants’ behaviour and the related energy performance analyses document about several strategies to monitor window operation, including different sensors and data series lengths. In this framework, the primary goal of this study is to propose effective guidelines for minimum experiment durations and their reliability. A six-year-long database from a living laboratory was used as a benchmark; and a recursive strategy enabled to split it into more than 2,500 subsets, supporting two main steps. First, information theory concepts were used to calculate uncertainty and subsets’ divergence were compared to the full database. Second, the subsets were used to train deep neural networks and evaluate the influence of monitoring lengths combined with different kinds of environmental data (i.e. indoor or outdoor). From the information-theoretic metrics, the results support that indoor-related variables can reduce most of the uncertainty related to window operation. Besides, subsets influenced by autumn and winter diverge the most compared to the full database. Considering the modelling approach, the results demonstrated that by including indoor-related variables, higher shares of reliably-performing models were achieved, and smaller subsets were needed. Seasonality has also played a major role along these lines. As a consequence, the conclusions supported the feasibility of nine-month-long field studies, starting in summer or spring, when indoor and outdoor variables are monitored.

Electricity consumption in the residential sector in Brazil has been increasing annually despite efforts to promote the energy efficiency of household appliances. One of the main goals for achieving more energy efficiency in dwellings is understanding its energy end uses. In this context, this paper presents a bottom-up model developed to analyse regional and national electricity end uses in the residential sector in Brazil based on a recent survey on Ownership of Appliances and Consumption Habits. The percentages of total electricity consumption associated with nine appliances (light bulbs, refrigerators, freezers, televisions, showers, microwaves, washing machines, fans, and air conditioners) were estimated. The values were obtained using the software EnergyPlus for air conditioners and electricity consumption equations for the other eight appliances. Results show that the proposed model gives reasonable estimates of electricity consumption, which were close to the values expected for most appliances. Regionally, the appliances for which ownership and pattern of use are influenced by the climate (electric showers, fans, and air conditioners) obtained the most significant variation in the percentage of electricity consumption.

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.