Prediction of indoor temperature and relative humidity using neural network models: model comparison |
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Authors: | Tao Lu Martti Viljanen |
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Affiliation: | (1) Laboratory of Structural Engineering and Building Physics, Department of Civil and Environmental Engineering, Helsinki University of Technology, P.O. Box 2100, 02015 Espoo, Finland |
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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. |
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Keywords: | Neural networks Indoor relative humidity prediction Indoor temperature prediction NNARX model Genetic algorithm Model validation |
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