Dataset for the metamodeling of naturally ventilated Brazilian low-cost houses to assess thermal performance
Description
Research objective: This study aimed to develop metamodels to assess the thermal discomfort in naturally ventilated Brazilian low-cost houses 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. The EnergyPlus outputs were post processed to assess the thermal comfort by means of the degree hours of discomfort by heat and by cold. 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.