Hourly Heating Load Prediction of Radiant Floor Heating System Based on the BP Neural Network

Xiang Li Li; Duan Mu Lin; Ren Jin Wang; Xing Wei Wang

doi:10.4028/www.scientific.net/AMR.243-249.4913

Paper Titles

Experimental Research on Performance of Water-Cooled VRF System in Winter in Shanghai
p.4891

Experimental Investigation of Indoor Thermal Environment of Using Radiant Cooling
p.4899

Design Range of Indoor Relative Humidity in Radiant Cooling Environment
p.4905

Experimental Study of a Solar Assisted Air Source Heat Pump in Heating Condition
p.4909

Hourly Heating Load Prediction of Radiant Floor Heating System Based on the BP Neural Network
p.4913

Comparison of Performance between R290 and R417A in Heat Pump Air Conditioning Water Heater Combination
p.4918

Simulation Study on the Wall Heat Transferring Characteristics for the Gas-Infrared Heated System
p.4923

Decoupling Control of Gas Mixing Based on Fuzzy-Single Neuron Adaptive PID
p.4929

Numerical Study on the Heat Transfer Characteristic in Periodical Variable Cross-Section Channel
p.4935

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 243-249Hourly Heating Load Prediction of Radiant Floor...

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.

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Periodical:

Advanced Materials Research (Volumes 243-249)

Pages:

4913-4917

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.243-249.4913

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Online since:

May 2011

Authors:

Xiang Li Li, Duan Mu Lin, Ren Jin Wang, Xing Wei Wang

Keywords:

BP Neural Network (BPNN), Dynamic Heating Load, Prediction, Radiant Floor Heating System

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