### PROBABILISTIC-STATISTICAL MODELING OF ANNUAL VARIATION OF OUTSIDE AIR TEMPERATURE AND ITS VALUES IN THE WARM SEASON

Pages 378-384

Subject: the ways for obtaining the sets of climate data for simulation of air and thermal regime of the building premises and assessment of its annual energy consumption are considered. It is noted that most modern approaches in this field rely on the concept of a “typical year”, and therefore unsuitable for engineering practice as they require the search, accumulation and selection of a large number of climatic parameter values. Research objectives: generalization of probabilistic approach to obtaining the sets of climatic data for the case of a study of annual variation in the average daily temperature of outdoor air and creation of a set of outdoor temperatures during the warm season (cooling period). Materials and methods: in this work, we used the software generation of climatic data sets by Monte Carlo method using a pseudorandom number generator based on a linear congruence algorithm. The regular seasonal variation of outside temperature is accounted for by using the “floating” mathematical expectation and the standard deviation. A numerical model of non-stationary thermal regime of a ventilated room is implemented based on the solution of a system of differential equations of heat conduction and heat transfer for the surfaces of the room. Results: some results of calculation of the current ambient temperature during the year and in the warm season using Monte Carlo method are presented for climatic conditions of Moscow. We performed comparison of the results of estimation of unsteady thermal regime of a ventilated room when using average daily outside air temperatures during a month obtained from climatic data and from the results of computer simulation. Conclusions: we demonstrated the principal coincidence of the statistical distribution of outside air temperature and temperature variation of the internal air for both compared variants. It is noted that Monte Carlo simulation gives the results that are indistinguishable, from the standpoint of engineering needs, from the use of a “typical year”, and we revealed the possibility of practical implementation of probabilistic-statistical principle of climate data generation for some calculations that concern the systems of air-conditioning and thermal regime of the building. It is proposed to apply the developed methodology for estimation of the annual energy consumption of buildings and for estimation of efficiency of energy and resource saving.

DOI: 10.22227/1997-0935.2018.3.378-384