کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11029483 | 1646508 | 2018 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Hot water temperature prediction using a dynamic neural network for absorption chiller application in Indonesia
ترجمه فارسی عنوان
پیش بینی دمای آب با استفاده از یک شبکه عصبی پویا برای کاربرد جذب کننده چیلر در اندونزی
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کلمات کلیدی
شبکه عصبی، چیلر جذب، تابش خورشیدی، آب گرم،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
چکیده انگلیسی
Weather condition particularly for solar radiation and dry bulb temperature has important role in absorption chiller performance. In this paper hot water temperature prediction in generator inlet of absorption chiller has been conducted under various weather conditions. Dry bulb temperature and global horizontal radiation are selected as predictors. Three artificial neural network (ANN) types including feed forward back-propagation, cascade forward back-propagation, and Elman back propagation models have been investigated for prediction. Moreover, numbers of neuron and time delay effects were analyzed to achieve an accurate prediction. The results show that hot water temperature in generator inlet can be predicted precisely using a feed forward back propagation neural network with the configuration of a three hour delayed input on radiation, current dry bulb temperature, seven neurons, tan-sigmoid transfer function and Bayesian regularization algorithm. The prediction results perform a good agreement between predicted and experimental values. The error resulting from training and validation is 3.1â¯Â°C and 2.6â¯Â°C with a coefficient of variation at 4.4% and 3.5% respectively.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Sustainable Energy Technologies and Assessments - Volume 30, December 2018, Pages 114-120
Journal: Sustainable Energy Technologies and Assessments - Volume 30, December 2018, Pages 114-120
نویسندگان
Nasruddin Nasruddin, Sholahudin Sholahudin, M. Idrus Alhamid, Kiyoshi Saito,