Paper Title:
Study on PMV Index Forecasting Method Based on Fuzzy C-Means Clustering
  Abstract

In order to improve the forecasting accuracy of indoor thermal comfort, the basic principle of fuzzy c-means clustering algorithm (FCM) and support vector machines (SVM) is analyzed. A kind of SVM forecasting method based on FCM data preprocess is proposed in this paper. The large data sets can be divided into multiple mixed groups and each group is represented by a single regression model using the proposed method. The support vector machines based on fuzzy c-means clustering algorithm (FCM+SVM) and the BP neural network based on fuzzy c-means clustering algorithm (FCM+BPNN) are respectively applied to forecast PMV index. The experimental results demonstrate that the FCM+SVM method has better forecasting accuracy compared with FCM+BPNN method.

  Info
Periodical
Advanced Materials Research (Volumes 383-390)
Chapter
Chapter 4: Composite Materials
Edited by
Wu Fan
Pages
925-930
DOI
10.4028/www.scientific.net/AMR.383-390.925
Citation
C. C. Zhang, X. G. Chen, Y. Q. Xu, "Study on PMV Index Forecasting Method Based on Fuzzy C-Means Clustering", Advanced Materials Research, Vols. 383-390, pp. 925-930, 2012
Online since
November 2011
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Price
$32.00
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