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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2020
عنوان انگلیسی مقاله:
Prediction of the ground temperature with ANN, LS-SVM and fuzzy LS-SVM for GSHP application
ترجمه فارسی عنوان مقاله:
پیش بینی دمای زمین با شبکه های عصبی، LS-SVM و LS-SVM فازی برای استفاده GSHP
منبع:
Sciencedirect - Elsevier - Geothermics, 84 (2020) 101757: doi:10:1016/j:geothermics:2019:101757
نویسنده:
Shiyu Zhoua,*, Xin Chub, Shubo Caob, Xiaoping Liub, Yucheng Zhoub
چکیده انگلیسی:
Ground source heat pump (GSHP) system has received more and more attentions for its energy-conserving and
environmental-friendly properties. Acquisition of the undisturbed ground temperature is the prerequisite for
designing of GSHP system. Measurement by burying temperature sensors underground is the conventional
means for obtaining the ground temperature data. However, this way is usually time consuming and high investment,
and also easily encounter with certain technical difficulties. The rapid development of intelligent
computation algorithm provides solutions for many realistic difficult problems. Basing on a great number of the
measured data of the ground temperature from two boreholes with 100m depth located in Chongqing, ground
temperature prediction models basing on artificial neural network (ANN) and support vector machine based on
least square (LS-SVM) are established, respectively. And then, two kinds of validation works, i.e., holdout validation
and k-fold validation are conducted toward the two models, respectively. Furthermore, a new method
that correlating fuzzy theory with LS-SVM is proposed to solve the big computation burden problem encountered
by LS-SVM model. By comparing with the above two models, it is concluded that the newly proposed model can
not only improve the calculation speed obviously but also be able to promote the prediction accuracy, especially
superior to the single LS-SVM model.
Keywords: Ground temperature | Fuzzy | Support vector machine | Ground source heat pump
قیمت: رایگان
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