Autors: Baeva, S. K., Stanev, R. H., Popov, S. A., Hinov, N. L.
Title: Stochastic model for prediction of microgrid photovoltaic power generation
Keywords: Forecast, Microgrid photovoltaic power generation, Data Mining, Stochastic model

Abstract: In this article, a stochastic model for prediction of microgrid photovoltaic power generation, using statistical and stochastic methods is presented. The study is performed in the following steps: Processing of a large database of historical data (Data Mining); Construction of a stochastic forecasting model; Reporting a symmetric mean absolute percentage error in forecasting.

References

    Issue

    47th International Conference Applications of Mathematics in Engineering and Economics (AMEE 2020), vol. 2333, issue 1, pp. 090020-1-090020-10, 2021, Bulgaria, AIP Conference Proceedings, https://doi.org/10.1063/5.0041825

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    Цитирания (Citation/s):
    1. Vira Shendryk, Yuliia Parfenenko, Yevhen Kholiavka, Petro Pavlenko, Oleksandr Shendryk, Larysa Bratushka, “Short-term Solar Power Generation Forecasting for Microgrid”, 3rd International Conference on System Analysis & Intelligent Computing (SAIC), Electronic ISBN:979-8-3503-9674-4, Print on Demand (PoD) ISBN:979-8-3503-9675-1, IEEE, 2022, https://doi.org/10.1109/SAIC57818.2022.9922982. - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus