کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
6727577 1428916 2018 42 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله ISI
Artificial neural network models using thermal sensations and occupants' behavior for predicting thermal comfort
ترجمه فارسی عنوان
مدل های شبکه عصبی مصنوعی با استفاده از حس گرهای حرارتی و رفتار ساکنان برای پیش بینی راحتی حرارتی
کلمات کلیدی
محیط داخلی آموزش مدل، جمع آوری داده ها، دمای هوا، رطوبت نسبی، سطح لباس، سرعت سوخت و ساز،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
It is important to create comfortable indoor environments for building occupants. This study developed artificial neural network (ANN) models for predicting thermal comfort in indoor environments by using thermal sensations and occupants' behavior. The models were trained by data on air temperature, relative humidity, clothing insulation, metabolic rate, thermal sensations, and occupants' behavior collected in ten offices and ten houses/apartments. The models were able to predict similar acceptable air temperature ranges in offices, from 20.6°C (69℉) to 25°C (77℉) in winter and from 20.6°C (69℉) to 25.6°C (78℉) in summer. The occupants' behavior in multi-occupant offices was more complex, which would lead to a slightly different prediction of thermal comfort. Since the occupants of the houses/apartments were responsible for paying their energy bills, the comfortable air temperature in these residences was 1.7°C (3.0℉) lower than that in the offices in winter, and 1.7°C (3.0℉) higher in summer. The comfort zone obtained by the ANN model using thermal sensations in the ten offices was narrower than the comfort zone in ASHRAE Standard 55, but that obtained by the ANN model using behaviors was wider than the ASHRAE comfort zone. This investigation demonstrates alternative approaches to the prediction of thermal comfort.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy and Buildings - Volume 174, 1 September 2018, Pages 587-602
نویسندگان
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