NDLI: Prediction of indoor temperature and relative humidity using neural network models: model comparison

Content Provider	Springer Nature Link
Author	Lu, Tao Viljanen, Martti
Copyright Year	2008
Abstract	The use of neural networks grows great popularity in various building applications such as prediction of indoor temperature, heating load and ventilation rate. But few papers detail indoor relative humidity prediction which is an important indicator of indoor air quality, service life and energy efficiency of buildings. In this paper, the design of indoor temperature and relative humidity predictive neural networks in our test house was developed. The test house presented complicated physical features which are difficult to simulate with physical models. The work presented in this paper aimed to show the suitability of neural networks to perform predictions. Nonlinear AutoRegressive with eXternal input (NNARX) model and genetic algorithm were employed to construct networks and were detailed. The comparison between the two methods was also made. Applicability of some important mathematical validation criteria to practical reality was examined. Satisfactory results with correlation coefficients 0.998 and 0.997 for indoor temperature and relative humidity were obtained in the testing stage.
Starting Page	345
Ending Page	357
Page Count	13
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	18
Issue Number	4
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-05-06
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural networks Indoor relative humidity prediction Indoor temperature prediction NNARX model Genetic algorithm Model validation Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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