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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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