Metamodels to Assess the Thermal Performance of Naturally Ventilated, Low-Cost Houses in Brazil

Research objective: This study aimed to develop metamodels to assess the thermal discomfort in a naturally ventilated Brazilian low-cost house during early design as a decision-making support framework, and for educational purposes. Method overview: The method encompassed a large number of simulations of the software EnergyPlus [EP] 8.1 using the Monte Carlo method. These simulations were used to develop a set of regression-based mathematical relationships between the inputs and the outputs. The Monte Carlo method was selected to help sample the many independent input variables, each with its own range of values, and form equally likely random combinations of input conditions for the energy simulation. Files description: The following data items were made available. (a) Parameter_Domains: Curitiba_parameterdomains.csv; Manaus_parameterdomains.csv and Sao_Paulo_parametersdomains.csv: CSV files created for each location. They contain a list of the 24 key parameters and random combinations of their values to create the input data for the 10,000 simulations. (b) Performance_Metrics: Curitiba_performancemetrics.csv; Manaus_performancemetrics.csv and Sao_Paulo_performancemetrics.csv: CSV files created for each location. They contain output values (outdoor and indoor discomfort by heat and by cold) for 10,000 simulations. (c) Sandbox: Sandbox.xlsx: Excel file for the application of the metamodels. It enables a quick and easy assessment of discomfort by heat and by cold for specific combinations of parameters, for each location. (d) Python_Codes: Python_Codes.zip: Compressed file consisting of the codes used to 1) run the simulations randomly combining values for each selected parameter within their specified ranges, and 2) to calculate the hours of discomfort by heat and by cold for each of the parameters’ combinations (10,000 simulations for each studied climate). (e) IDFs_Base: Curitiba_idfbase.idf; Manaus_idfbase.idf; Sao_Paulo_idfbase.idf : Input Data File (IDF) created for each location. They contain the description of all input data considered in an annual building performance simulation.

研究目标：本研究旨在构建元模型(metamodels)，以在早期设计阶段评估巴西自然通风低成本住宅的热不舒适状况，以此作为决策支持框架，并用于教育用途。
方法概述：本方法依托蒙特卡洛方法，开展了大量基于EnergyPlus[EP] 8.1软件的模拟实验。通过上述模拟，构建了输入变量与输出变量间的多组基于回归分析的数学关系式。选择蒙特卡洛方法，旨在对多个独立输入变量（各变量拥有独立的取值范围）进行采样，并生成等概率的输入条件随机组合，用于建筑能耗模拟。
文件说明：本次公开了以下数据项：
(a) 参数域文件：Curitiba_parameterdomains.csv、Manaus_parameterdomains.csv及Sao_Paulo_parametersdomains.csv，为各城市单独生成的CSV格式文件。其包含24项关键参数的列表，以及各参数取值的随机组合，用于生成10000次模拟所需的输入数据。
(b) 性能指标文件：Curitiba_performancemetrics.csv、Manaus_performancemetrics.csv及Sao_Paulo_performancemetrics.csv，为各城市单独生成的CSV格式文件。其包含10000次模拟的输出结果（室外与室内的热不舒适、冷不舒适时长）。
(c) 沙盒工具文件：Sandbox.xlsx：用于元模型应用的Excel文件。可针对各城市的特定参数组合，快速便捷地评估热不舒适与冷不舒适状况。
(d) Python代码文件：Python_Codes.zip：压缩包文件，内含两类代码：1）在指定参数范围内随机组合各参数取值以运行模拟的代码；2）针对每组参数组合（每个气候区域开展10000次模拟）计算热不舒适与冷不舒适时长的代码。
(e) 基础IDF文件：Curitiba_idfbase.idf、Manaus_idfbase.idf、Sao_Paulo_idfbase.idf：为各城市单独生成的输入数据文件(Input Data File, IDF)。其包含年度建筑能耗模拟中所涉及的全部输入数据描述。