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.