Paper Title:
Hourly Heating Load Prediction of Radiant Floor Heating System Based on the BP Neural Network
  Abstract

An improved error transfer BP neural network model is use to predict the dynamic heating load in a house or a dwelling unit with the character of hour heating load. Compared with the conventionally physical model, the computation consumption is reduced greatly for the less number of the parameters by improving the error transfer ways. The numerical simulation and experimental measure in a low energy consumption building of Dalian city are performed and the BP neural network model was based entirely on the field survey data. The results show that the simulated results are well agreed with the experimental data and the averaged relative error is less than 5%. Furthermore, this improved model can predict accurately hour heating load during the course of next 24 hours and it is favorable for predicting the short time heating load problems.

  Info
Periodical
Advanced Materials Research (Volumes 243-249)
Edited by
Chaohe Chen, Yong Huang and Guangfan Li
Pages
4913-4917
DOI
10.4028/www.scientific.net/AMR.243-249.4913
Citation
X. L. Li, D. M. Lin, R. J. Wang, X. W. Wang, "Hourly Heating Load Prediction of Radiant Floor Heating System Based on the BP Neural Network", Advanced Materials Research, Vols. 243-249, pp. 4913-4917, 2011
Online since
May 2011
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$32.00
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