SYSTEM OF FORECASTING THE SMART HOME SOLAR BATTERY POWER

Igor Olenych, M. Pavlyk, M. Martsiniv

Abstract


Controlling household appliances in automatic mode according to the specified logic and the needs of the residents of a smart home allows efficient use of electricity and saves time and money. The paper proposes a system of forecasting the smart home solar battery power based on the analysis of weather data. The cloudiness degree values obtained using the OpenWeatherMap online service for a given area and time are used. Forecasting is carried out by methods of correlation analysis. The Pearson coefficient and parameters of the regression line were determined using the experimentally measured power of the Buheshui JZ-110X69 photovoltaic module under different weather conditions. The close to one value of Pearson coefficient modulus indicates a close relationship and high correlation between cloudiness and solar panel power.

Correction coefficients were introduced to eliminate the influence of the angle between sun rays and the normal to the panel surface on the photovoltaic module power. The correction coefficients were determined on a cloudless day on March 20, 2022, in Lviv for each hour as the ratio of the solar module power at the measurement time to the measured at 1 pm power. It was established that adjusting the photovoltaic module power value according to the time of day increases the degree of linear correlation and the angle of inclination of the regression line.

The weather forecast and obtained linear regression equation were used to estimate the future power of the solar battery. The use of correction coefficients according to the time of day increases the accuracy of the smart home solar battery power forecast. The obtained results can be used to form recommended scenarios for the rational use of energy resources. Choosing the optimal scenarios for using household devices in accordance with the forecasted solar battery power makes it possible to balance the comfort necessary level and frugal energy management of a smart home.

Key words:  smart home, energy management, energy efficiency, correlation, regression line, forecasting.


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DOI: http://dx.doi.org/10.30970/eli.18.2

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