Performance-based design using computational and parametric optimization is an effective strategy to solve the multiobjective problems typical of building design. In this sense, this study investigates the developing process of parametric modeling and optimization of a naturally ventilated house located in a region with well-defined seasons. Its purpose is to improve its thermal comfort during the cooling period by maximizing Natural Ventilation Effectiveness (NVE) and diminishing annual building energy demand, namely Total Cooling Loads (TCL) and Total Heating Loads (THL). Following a structured workflow, divided into (i) model setting, (ii) Sensitivity Analyses (SA), and (iii) Multiobjective Optimization (MOO), the process is straightforwardly implemented through a 3D parametric modeling platform. After building set up, the input variables number is firstly reduced with SA, and the last step runs with an innovative model-based optimization algorithm (RBFOpt), particularly appropriate for time-intensive performance simulations. The impact of design variables on the three-performance metrics is comprehensively discussed, with a direct relationship between NVE and TCL. MOO results indicate a great potential for natural ventilation and heating energy savings for the residential building set as a reference, showing an improvement between 14–87% and 26–34% for NVE and THL, respectively. The approach meets the current environmental demands related to reducing energy consumption and CO2 emissions, which include passive design implementations, such as natural or hybrid ventilation. Moreover, the design solutions and building orientation, window-to-wall ratio, and envelope properties could be used as guidance in similar typologies and climates. Finally, the adopted framework configures a practical and replicable approach for studies aiming to develop high-performance buildings through MOO.
The data set compiled in this file refers to the Multizone EnergyPlus model, used in the investigations of the research article entitled "Natural ventilation potential from weather analyses and building simulation". The technical information regarding the model has been grouped into tables, which include: the general simulation settings, the properties of the building materials, the Airflow Network opening settings used in the annual investigation, in addition to the controls established in the Energy Management System (EMS) for hybrid ventilation system operation. The user behaviour, regarding the living and bedrooms occupancy schedule, is also presented in a graph. This data set is made available to the public to clarify details of the EnergyPlus model and how the hybrid operation was defined. In this way, other researchers can perform an extended analysis of the information.
Este estudo tem por objetivo investigar a incerteza do método de simulação da NBR 15575-1 (ABNT, 2013) nos resultados dos níveis de classificação de desempenho térmico de habitações. A investigação compreendeu análise do desempenho térmico de uma habitação por simulação computacional no programa EnergyPlus, para o clima de Florianópolis - SC. Foram analisadas variáveis imprescindíveis para a definição de um dia típico, as quais são desconsideradas pelo método de simulação da norma, como data do dia típico de verão e inverno, velocidade e direção do vento, algoritmo de cálculo da irradiação solar e tipo de céu. Tais variáveis foram utilizadas em projeto de experimento estatístico com combinação fatorial para determinar a incerteza nos resultados e a sensibilidade das variáveis no nível de classificação de desempenho. As simulações foram realizadas com nove modelos diferentes de envelope da habitação. Os resultados mostraram que os piores níveis de classificação de desempenho, tanto no verão quanto no inverno, foram os modelos sem contato com o solo. A variável de maior influência para a análise no verão e inverno, para todos os modelos de envelope analisados, foi o tipo de céu. Pode-se concluir que as variáveis desconsideradas pela NBR 15575-1 (ABNT, 2013) na criação de um dia típico para simulação exercem considerável influência nos resultados dos níveis de classificação do desempenho térmico, gerando imprecisão nos resultados.
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