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.

When used as building construction elements, photovoltaic (PV) systems can be multifunctional. Therefore, they can provide innovative envelope designs that impact energy consumption. PV integration can radically change the form of buildings and cities; thus, their architectural quality must be protected. This work evaluates the integration quality of PV systems, considering constructive, functional, and aesthetical (formal) aspects, contemplating performance criteria set by different stakeholders. The influences of PV integration on the energy performance of buildings, its energy generation, energy yield, Performance Ratio, economic viability, and aesthetical features are assessed. Widely used types of integration are contemplated: PV rooftops and PV shading devices. The approach is described in detail and uses the internationally adopted softwares EnergyPlus®, Rhino®, and PVsyst® to be broadly used by designers. Six case studies of a commercial building in low-latitude sites showed that PV building integration can be used as a strategy to reduce energy consumption, while keeping high energy generation performance, being economically viable, and respecting architecture. Results have emphasized the importance of assessing PV architectural integration quality, showing that a detailed analysis is essential to identify more advantageous situations.

Energy benchmarking models assist the decision-making process for urban planners, architects, engineers and stakeholders. However, specialists diverge about the accuracy of benchmarking models used to represent a location. The aim of this study is to compare two benchmarking models of nursery schools: first, considering the national building stock for Brazil; second, using local data for a Southern Brazilian city. The national benchmarking model was obtained from a country-level benchmarking policy. The local benchmarking model was developed in four steps: data collection, archetype modeling, energy simulations and multiple linear regression to formulate the benchmark equation. Then, both national and local models were compared using data of 12 actual nursery schools, through their energy performance classification and an ANOVA test. Results showed that there are no significant differences between the models. Conclusion points out that a country-level benchmarking can adequately represent a city-level benchmarking; however, a late clustering process might be indicated for cases in locations with particularities apart from the national building stock. This paper provides an opportunity to discuss if benchmarking models, created with large stock-level analysis, indeed can evaluate the energy performance of specific buildings. The authors recommend future research comprising other regions to enhance applicability of the results.

The use of personal fans allows improving thermal comfort and energy savings in warm office spaces. This is due to individual adjustment and extended indoor temperature acceptability. However, to achieve that, the usability of fans must be assured. Therefore, an experiment with 40 people of various age groups was carried out to assess four types of fans, one of which is an evaporative cooling device. The goal was to find out which criteria should be used for selecting a fan to implement in an office space. Results show that airflow sensation and speed adjustment are considered the most important, although, noise is also very important, and cost can be an eliminatory criterion. The evaporative device was the best rated even in a space with 70 to 80% relative humidity, as users considered it to have a smooth controllable airflow. The results highlight these aspects should be considered in the selection of a personal fan and could also drive the industry to improve fans design for increasing usability and expanding the use of these systems.

Solar radiation along with other weather variables are commonly processed on typical meteorological years (TMYs) to be applied in the design of various energy systems. However, in several regions of the world, solar radiation data usually lacks a suitable and/or representative measurement, which leads to its modeling and prediction to properly fill this information in the databases. Consequently, the accuracy of these models can influence the viability and proper design of such energy systems. Within this context, the present contribution aims to assess the quality of solar radiation data included in the most recent TMY databases with Brazilian data and how that quality can influence the selection of months that create TMYs as well as the building performance simulation (BPS) results. Because two different approaches to generate the solar radiation data are used, we evaluate the global horizontal irradiation data in the two latest versions of recent Brazilian TMY databases against the corresponding satellite-derived ones obtained from the POWER database (NASA). Simultaneously, as another alternative approach, global solar radiation data are calculated for the same studied locations and period through the modeling method used to generate the current version of the International Weather for Energy Calculations (IWEC2), and its performance is also compared against the corresponding reanalysis data (POWER). Finally, a set of case studies applying the local building performance regulations are exhaustively analyzed to quantify the impact of the uncertainty of solar radiation models on BPS results throughout Brazil. The results indicate that the accuracy of solar radiation models can highly influence the resulting TMY configurations. These changes can drive differences up to 40% on the prediction of the ideal annual loads of the residential buildings while, regardless of design performance, differences lower than 10% are found for the commercial case studies in most locations. Conversely, the prediction of peak loads for cooling shows to be more sensitive to the climate data changes in the commercial buildings than in the residential ones.

Information on building energy consumption and its characteristics is essential for carrying out benchmarking processes. However, currently a lack of data acts as a major barrier in this regard. To address this issue, the purpose of this study is to demonstrate the application of machine learning to estimate building energy use intensities of bank branches buildings located in Brazil. The methodology proposed in this study completed a data collection regarding the bank branch typology. Then, the archetype model and its fixed and variables inputs variables were defined to generate 48,000 samples that were simulated using EnergyPlus program. A Sobol sensitivity analysis was performed, showing that the lighting power density followed by the weather variable were found to be the most influential variables when estimating the energy consumption of the bank branches. Finally, a comparison between machine learning techniques were applied to train the predictive model. The Support Vector Machine achieved MAE and RMSE values of 3.16 and 4.45 kWh/m².year, respectively, representing the most accurate model for benchmarking purposes. Due to the non-linearity among the variable parameters, optimizing sophisticated machine learning techniques significantly improved the model accuracy. The results are of great value, since the model developed can be used in future benchmarking throughout the country. The methodology showed high accuracy and could be extended to other typologies.

O crescente aumento de consumo energético em edificações é uma questão preocupante. O modo como as edificações se comportam em relação ao clima em que estão inseridas é importante no seu consumo final, e assim, diretrizes iniciais de projeto voltadas ao seu comportamento térmico podem reduzir boa parte do consumo durante a operação. Isso porque essas diretrizes visam otimizar o desempenho das edificações, minimizando o uso de equipamentos de resfriamento para garantir o conforto dos usuários. No entanto, essas análises de desempenho térmico são realizadas com o projeto já consolidado, dificultando o alcance de bons níveis de desempenho e sem grande margem de alteração de projeto com este foco. Visando unir o processo a essas análises, o objetivo deste trabalho é o levantamento de dados sobre o processo de projeto de arquitetos para compreender quais parâmetros de desempenho já são considerados, quais outros poderiam ser incorporados, e quais as fases de maior relevância do ponto de vista térmico. Percebeu-se que os participantes deste estudo, apesar de considerarem relevante a avaliação de critérios como insolação e ventilação natural na concepção de projeto, o fazem com métodos pouco precisos. Além disso, evidenciou-se o desconhecimento de aspectos importantes, como a NBR 15575, a etiqueta de eficiência energética e o uso de simulações computacionais. Portanto, propõe-se uma estrutura de trabalho integrando as respostas sobre definições de projeto com os critérios de análise de desempenho. O desenvolvimento de uma estrutura de trabalho permitirá futuros experimentos de inserção de simulações no processo de projeto para validação.

Climate change mitigation strategies are multifaceted and require collaboration among a range of stakeholder groups. The objective of this paper was to develop an overarching Renewable Energy and Energy Conservation Area Policy (REECAP) framework. The framework was developed based on a comprehensive literature review, in which seven principles for Renewable Energy and Energy Conservation Policies were identified. The paper also includes a case study to demonstrate an application of the REECAP framework. The novelty of the framework stems from its integration of carbon-energy-cash flows among different decision-making spheres, scales and area specific characteristics. The framework provides a mathematical understanding of how energy strategies can be transformed and optimised in a cost-effective manner by integrating stakeholders under a shared vision.