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Microsystems, Electronics and Acoustics
ISSN 2523-4447 · e‑ISSN 2523-4455 Open Access · CC BY-SA 4.0
Vol. 24 · Issue 2 · 2019 Apr 30, 2019 Electronic Systems and Signals

Estimation of the energy level of the wind stream for the accompanying data

MY
Mykhailo Kostiantynovych Yaremenko Corresponding Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського» Ukraine
KK
Kateryna Serhiivna Klen Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського» Ukraine
Pages56-63 PublishedApr 30, 2019
PDF (Ukrainian)
VOL 24 · ISS 2 · 2019 View issue

Abstract

In the article the matrix of Pearson correlation coefficients for wind speed, air temperature, pressure at sea level and time on meteorological data taken from 19.02.2019-27.02.2019 in Kyiv was calculated. The matrix was calculated for the size of the sample equal to 24. The significance of the coefficients according to Student's t-criterion was determined, that the value of the correlation coefficients and their significance depend on the concrete sample. It is concluded that due to the stochastic nature of the wind mass movement, the amount of solar heat received and other parameters, the significance of the correlation coefficients of the investigated values and their magnitude may vary depending on the time and day of observations. Graphs of changes in correlation coefficients between wind speed and time, between wind speed and pressure, between wind speed and temperature, between temperature and pressure, depending on the number of observations, have been constructed, the character of the change and the required minimum number of observations have been found. Near Fourier with 7-8 harmonics, graphs of functions of correlation coefficients between wind speed and time, between wind speed and pressure using the Matlab Curve Fitting Toolbox application are approximated. The number of harmonics was chosen with the best approximation of the approximated graph to the original. According to the approximated graphs, a graph of correlation coefficient correlation between the coefficient of correlation of wind speed and the coefficient of correlation of wind speed and pressure was constructed. The obtained results show that the nature of the correlation coefficients between the coefficient of correlation of wind speed and time and the correlation coefficient between pressure and wind speed allows predicting the values of these meteorological data. Ref. 19, fig. 13, tabl. 4

Keywords

#systems of distributed generation #weather data #forecasting #correlation coefficient #approximation #Fourier series

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