The climate change assumes nowadays on significance. Weather data may be available on various time scales – long-term, minutes, hours, days, periods, years. Measurements of air temperature are realized for a long time. Data in Slovakia are available from few weather stations of Slovak Hydrometeorological Institute (SHMI). For long-term and wide-ranging measurement of various parameters this can be complicated and expensive. This paper is focused on temperature measurement near the experimental laboratory. Estimation of daily, monthly and yearly mean temperatures is done in different ways. Results from experimental temperature measurement, in a way of selection of unusual extremes are presented. Shorter recording intervals describe the temperature courses in a more pertinent way.
Summer overheating is usually caused by a combination of factors, such as building location, orientation, fabric, ventilation and occupancy behaviour. People by their behaviour influence overheating through the internal heat gain mode and window opening ventilation mode. The article describes several types of occupancy profiles according to their behaviour taken from the literature: the profile of the worker, the child, the pensioner and the adventurer. From them, it is possible to disable the heat gain schedule for the whole household or apartment house. Heat gains from appliances are updated according to the modern lifestyle and current household appliances. The effect of heat gains in combination with other factors, which caused overheating, is further evaluated through the building simulation program.
Scientific research in the area of building simulations has a great potential and it is continuously developing and advancing. Computer simulations are helpful in many areas of Civil Engineering, such as energy demand, moisture transport, thermal comfort, ventilation etc. Climate data measured by experimental weather station are analyzed in this article. Weather station is located within the University campus and data recorded with a short are used in a non-steady heat-air-moisture simulation. Climate parameters differences caused by the various averaging periods are shown. This differences are also analyzed in term of outdoor surface temperatures calculated with WUFI Pro simulation software.
In the paper, we statistically analysed data on the average hourly wind speed obtained from the meteorological station Poprad (located at the Poprad-Tatry airport, the Prešov region, Northern Slovakia) for the period 2005–2021. High altitude and rough mountainous terrain influence the weather conditions considerably and are a source of occasional weather risks. Finding an appropriate wind speed distribution for modelling the wind speed data is therefore important to determine the wind profile at this particular location. In addition to the commonly used two- and three-parameter Weibull distribution, a more flexible exponentiated Weibull (EW) distribution was applied to model the wind speed. Based on the results of the goodness-of-fit criteria (the Kolmogorov–Smirnov test, the Anderson–Darling test, Akaike’s and Bayesian information criteria, the root mean square error, and the coefficient of determination), the EW distribution obtained a significantly better fit to seasonal and monthly wind speed data, especially around the peaks of the data. The EW distribution also proved to be a good model for data with high positive skewness. Therefore, we can recommend the EW distribution as a flexible distribution for modelling a dataset with extremely strong winds or outliers in the direction of the right tail. Alongside the wind speed analysis, we also provided the wind direction analysis, finding out that the most prevailing direction was west (W)—with an occurrence rate of 34.99%, and a mean wind speed of 3.91 m/s, whereas the northern (N) direction featured the lowest occurrence rate of only 4.45% and the mean wind speed of 1.99 m/s.
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