Autors: Popov, S. A., Stanev, R. H., Baeva, S. K., Hinov, N. L.
Title: Stochastic model for microgrid load forecasting
Keywords: Forecast, Microgrid load, Statistical and stochastic methods

Abstract: Forecasting is a special scientific study, the subject of which is the perspective for the development of the phenomenon. It is of great importance for many scientific and applied fields. This article presents a stochastic model for microgrid load forecasting. Statistical and stochastic methods are used for forecasting.



    47th International Conference Applications of Mathematics in Engineering and Economics (AMEE 2020), vol. 2333, issue 1, pp. 090022-1-090022-10, 2021, Bulgaria, AIP Conference Proceedings,

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    Цитирания (Citation/s):
    1. Zilong Zhao, Jinrui Tang, Jianchao Liu, Ganheng Ge, Binyu Xiong, Yang Li, "Short-term microgrid load probability density forecasting method based on k-means-deep learning quantile regression", Energy Reports 8 (2022) 1386–1397, - 2022 - в издания, индексирани в Scopus или Web of Science
    2. B. Deepanraj, N. Senthilkumar, T. Jarinc, Ali Etem Gurel, L. Syam Sundar, A. Vivek Anand, “Intelligent Wild Geese Algorithm with Deep Learning Driven Short Term Load Forecasting for Sustainable Energy Management in Microgrids”, Sustainable Computing: Informatics and Systems, Available online 18 October 2022, 100813, - 2022 - в издания, индексирани в Scopus или Web of Science

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