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

被引：0
作者：
Tao Lu
Martti Viljanen
机构：
[1] Helsinki University of Technology,Laboratory of Structural Engineering and Building Physics, Department of Civil and Environmental Engineering
来源：
Neural Computing and Applications | 2009年 / 18卷
关键词：
Neural networks; Indoor relative humidity prediction; Indoor temperature prediction; NNARX model; Genetic algorithm; Model validation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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