نوع مقاله : مقاله پژوهشی
نویسندگان
1 موسسه غیر انتفاعی آپادانا، شیراز، ایران
2 دانشگاه تهران
3 دانشگاه علم و صنعت ایران، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Global warming due to building industry and carbon emission is one of the major concerns around the world. Researchers have come to this conclusion that it is essential to manage and reduce the building energy consumptions and lead the designings and constructions to sustainability approaches. Exterior shading devices are one of the most important and practical parameters for sustainable passive architectural design especially in hot climates. They can affect controlling the energy resources in building performances inclusive thermal load, daylight and adaptive thermal comfort. The application of the shadings is highly effective when the shading's design parameters have carefully and accuratly studied and designed because an inefficient shading device can easily increase the thermal load and make the problems with glare or darkness at the same time or while keeping the daylight indicators in the standard domains, increasing the energy consumptions. Although a lot of studies have investigated the design parameters of exterior shading devices containing dimensions, materials, and the location of installing them thrugh optimization methods, it seems that none of the researches have considerd the effect of the shading quality on building performances. To investigating the quality of shading in this paper, through field measurement in a residential building in Shiraz, Iran, a model is simulated and validated and then a novel parametric exterior fixed shading device added to the model is created in grasshopper plugin which is able to produce a variety range of shadings. By using the LHS thecnique to produce an outspread community of shading samples and implementing the energy simulation for each sample, a big dataset of 13600 samples of different shadings is developed to train and test an Artificial Neural Network (ANN). This ANN has been applied as a fast emulator and the searching space for multy-objective optimization through NSGA_III algorithm. Five functions as independent variables containing the minimum of cooling and heating load, the minimum of ASE (Annual Sun Exposure), the maximum of sDA (special Daylight Autonomy) and UDI (Useful Daylight Illuminance) are considerd as the main objectives in the optimization process. Finally the Pareto front solutions have been classified on the base of the Percent of Time Comfortable (PTC) using adaptive thermal comfort model. This classification perfectly shows that although the optimized shading devices can reduce the cooling load up to 53% -73%, reduce the heating load up to 8% -10% and keep the daylight standard indicators (ASE, sDA and UDI) in the acceptable domains, the values of PTC could be different from 33.3% (means 4 months of the year) to 66.67% (means 8 months of the year). This variety of PTC value is considerable becuase while some optimized shading devices can keep the PTC in the interior space equal to 33.3%, the other cases with the same rate of energy saving and daylight standard indicators can improve the PTC value to 66.67%. Therefore this paper introduces the PTC in adaptive thermal comfort model as a new metric to evaluating the quality of the shading produced by any shading devices types.
کلیدواژهها [English]
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