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Forecasting day-ahead of power generation from photovoltaic stations and use weather apps

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This article presents the results of research on the influence of meteorological parameters on the amount of electricity generated by photovoltaic plants. The dependences of the amount of energy generated by the photovoltaic station on the time of generation and the length of daylight, the average daily temperature and the number of clear hours during the day are given. We proposed to use cloud level coefficients to reflect changes in solar insolation. Based on the obtained data, a new mathematical model of the daily forecast of electricity generation of photovoltaic stations is created. Weather forecast applications are recommended to determine the input parameters of the forecast model.
Rocznik
Strony
143--149
Opis fizyczny
Bibliogr. 9 poz., rys., wykr., wzory
Twórcy
  • Ivano-Frankivsk National Technical University of Oil and Gas, Ukraine, 15 Karpatska Str., Ivano-Frankivsk, 76019, Ukraine
autor
  • Ivano-Frankivsk National Technical University of Oil and Gas, Ukraine, 15 Karpatska Str., Ivano-Frankivsk, 76019, Ukraine
  • Ivano-Frankivsk National Technical University of Oil and Gas, Ukraine, 15 Karpatska Str., Ivano-Frankivsk, 76019, Ukraine
Bibliografia
  • [1] Kryzhanivskyi Ye., Koshlak H., Obtaining porous thermal insulating materials based on ash from thermal power plants. Journal of New Technologies in Environmental Science, 2020, Vol. 4, No. 1, pp. 3-12.
  • [2] Advanced Inverter Functions to Support High Levels of Distributed Solar. Policy and Regulatory Considerations (Brochure). Imprint. Washington, United States. Dept. of Energy. Office of Solar Electric Technology; Oak Ridge, Tenn.: distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy. 2014, pp. 1-8. Retrieved from: https://www.nrel.gov/docs/fy15osti/62612.pdf.
  • [3] Reindl T., Walsh W., Yanqin Z., Bieri M., Energy meteorology for accurate forecasting of PV power output on different time horizons, Energy Procedia, 2017. No. 130, pp. 130-138, https://doi.org/10.1016/j.egypro.2017.09.415.
  • [4] Batsala Ya.V., Hlad I.V., Yaremak I.I., Kiianiuk O.I., Mathematical model for forecasting the process of electric power generation by photoelectric stations. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2021, No. 1, pp. 111-116. https://doi.org/10.33271/nvngu/2021-1/111.
  • [5] Riahi J., Vergura S., Mezghan D., Mami A., Intelligent Control of the Microclimate of an Agricultural Greenhouse Powered by a Supporting PV System. Applied Sciences. 2020. No. 10(4): 1350. https://doi.org/10.3390/app10041350.
  • [6] WetterRadar & Warnungen. Wetter Online Meteorologische Dienstleistungen GmbH. Retriewed from: https://www.weatherandradar.com/apps/.
  • [7] Meteoblue delivers local weather information (n.d.). Retrieved from https://www.meteoblue.com/.
  • [8] Fedoriv M.Y., Increasing reliability and energy efficiency of electric driven boring units. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2017, 2, pp. 93-98.
  • [9] Sunny Portal. PV System Data. Retriewed from: https://www.sunnyportal.com/Templates/PublicPage.aspx?page=0fa455f6-64b8-4475-b66e-b01cb5a0836d
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-83bf286d-26b1-4825-8588-547ec9ad80b1
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