Data-driven and numerical approaches to predict thermal comfort in traditional courtyards

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{TESHNEHDEL:2020:SETA,

• author = "Saeid Teshnehdel and Seyedasghar Mirnezami and Aniseh Saber and Ali Pourzangbar and Abdul Ghani Olabi",
• title = "Data-driven and numerical approaches to predict thermal comfort in traditional courtyards",
• journal = "Sustainable Energy Technologies and Assessments",
• volume = "37",
• pages = "100569",
• year = "2020",
• ISSN = "2213-1388",
• DOI = "doi:10.1016/j.seta.2019.100569",
• URL = "http://www.sciencedirect.com/science/article/pii/S2213138819305697",
• keywords = "genetic algorithms, genetic programming, Thermal comfort, PET, PMV, Traditional courtyards",
• abstract = "This paper studies the climactic performance of the 10 traditional courtyards located in warm-dry climates of Kashan and cold climates of Ardabil based on shading and sunlit coverage. The modelling process comprises two sections: first, a number of numerical simulations are run using Envi-met software to detail the shading and sunlit percentage, PET and PMV in the samples of interest. These numerical models are validated on the basis of the results made available by field observations. Such validation revealed an excellent agreement between the numerical solution and the benchmarking data. Afterwards, GP is used to evolve some equations for predicting PET and PMV using the data points derived from the numerical simulations. The results suggest that regarding the thermal indices (PET and PMV), there is a high correlation between the shadow and sunlit effects and thermal comfort in Kashan's houses in comparison with Ardabil houses. However, in tropical regions (Kashan), summer shading and winter sunlit have a greater effect on thermal comfort and temperature adjustment than cold regions. Moreover, the statistical criterion, as well as reliability analysis and contour plots show that the GP developed formulas can be exploited in predicting the PET and PMV based on the shading percentage",

}

Genetic Programming entries for Saeid Teshnehdel Seyedasghar Mirnezami Aniseh Saber Ali Pourzangbar Abdul Ghani Olabi

Citations