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Ensemble Machine Learning Model for Day Ahead Solar Power Forecasting for Mongolia Power System: book chapter. / Rusina, Anastasia; Osgonbaatar, Tuvshin; Stepanova, Alina et al.
Proceedings of the 2023 Belarusian-Ural-Siberian Smart Energy Conference, BUSSEC 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. p. 84-87.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Rusina, A, Osgonbaatar, T, Stepanova, A & Matrenin, P 2023, Ensemble Machine Learning Model for Day Ahead Solar Power Forecasting for Mongolia Power System: book chapter. in Proceedings of the 2023 Belarusian-Ural-Siberian Smart Energy Conference, BUSSEC 2023: book. Institute of Electrical and Electronics Engineers Inc., pp. 84-87, 2023 Belarusian-Ural-Siberian Smart Energy Conference (BUSSEC), Екатеринбург, Russian Federation, 25/09/2023. https://doi.org/10.1109/BUSSEC59406.2023.10296344

APA

Rusina, A., Osgonbaatar, T., Stepanova, A., & Matrenin, P. (2023). Ensemble Machine Learning Model for Day Ahead Solar Power Forecasting for Mongolia Power System: book chapter. In Proceedings of the 2023 Belarusian-Ural-Siberian Smart Energy Conference, BUSSEC 2023: book (pp. 84-87). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BUSSEC59406.2023.10296344

Vancouver

Rusina A, Osgonbaatar T, Stepanova A, Matrenin P. Ensemble Machine Learning Model for Day Ahead Solar Power Forecasting for Mongolia Power System: book chapter. In Proceedings of the 2023 Belarusian-Ural-Siberian Smart Energy Conference, BUSSEC 2023: book. Institute of Electrical and Electronics Engineers Inc. 2023. p. 84-87 doi: 10.1109/BUSSEC59406.2023.10296344

Author

Rusina, Anastasia ; Osgonbaatar, Tuvshin ; Stepanova, Alina et al. / Ensemble Machine Learning Model for Day Ahead Solar Power Forecasting for Mongolia Power System : book chapter. Proceedings of the 2023 Belarusian-Ural-Siberian Smart Energy Conference, BUSSEC 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 84-87

BibTeX

@inproceedings{84c054128bbf4bab88b90eab5e072c5d,
title = "Ensemble Machine Learning Model for Day Ahead Solar Power Forecasting for Mongolia Power System: book chapter",
abstract = "The efficient and sustainable use of renewable energy sources requires an accurate power generation forecasting. This paper considers the application and comparison of ensemble algorithm based on decision tree for solar power forecasting for Mongolia power system. The input data for the machine learning model are historical data on power generation for 2019-2021 years of six photo-voltaic power plants operating in central power system of Mongolia and meteorological data in these power plants. Ensemble machine learning model allows to determine the non-linear and non-stationary dependence of the time series and besides can be implemented in the task of forecasting the daily generation schedule. The proposed model creates a day ahead forecast of hourly generation curve of the photo-voltaic power plants under consideration with a normalized absolute percentage error 6.5 - 8.4%. Increasing the accuracy of solar power forecasting can positively affect the operation and planning of the central power system of Mongolia. {\textcopyright} 2023 IEEE.",
author = "Anastasia Rusina and Tuvshin Osgonbaatar and Alina Stepanova and Pavel Matrenin",
note = "The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully.; 2023 Belarusian-Ural-Siberian Smart Energy Conference (BUSSEC) ; Conference date: 25-09-2023 Through 29-09-2023",
year = "2023",
doi = "10.1109/BUSSEC59406.2023.10296344",
language = "English",
isbn = "979-835035807-0",
pages = "84--87",
booktitle = "Proceedings of the 2023 Belarusian-Ural-Siberian Smart Energy Conference, BUSSEC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS

TY - GEN

T1 - Ensemble Machine Learning Model for Day Ahead Solar Power Forecasting for Mongolia Power System

T2 - 2023 Belarusian-Ural-Siberian Smart Energy Conference (BUSSEC)

AU - Rusina, Anastasia

AU - Osgonbaatar, Tuvshin

AU - Stepanova, Alina

AU - Matrenin, Pavel

N1 - The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully.

PY - 2023

Y1 - 2023

N2 - The efficient and sustainable use of renewable energy sources requires an accurate power generation forecasting. This paper considers the application and comparison of ensemble algorithm based on decision tree for solar power forecasting for Mongolia power system. The input data for the machine learning model are historical data on power generation for 2019-2021 years of six photo-voltaic power plants operating in central power system of Mongolia and meteorological data in these power plants. Ensemble machine learning model allows to determine the non-linear and non-stationary dependence of the time series and besides can be implemented in the task of forecasting the daily generation schedule. The proposed model creates a day ahead forecast of hourly generation curve of the photo-voltaic power plants under consideration with a normalized absolute percentage error 6.5 - 8.4%. Increasing the accuracy of solar power forecasting can positively affect the operation and planning of the central power system of Mongolia. © 2023 IEEE.

AB - The efficient and sustainable use of renewable energy sources requires an accurate power generation forecasting. This paper considers the application and comparison of ensemble algorithm based on decision tree for solar power forecasting for Mongolia power system. The input data for the machine learning model are historical data on power generation for 2019-2021 years of six photo-voltaic power plants operating in central power system of Mongolia and meteorological data in these power plants. Ensemble machine learning model allows to determine the non-linear and non-stationary dependence of the time series and besides can be implemented in the task of forecasting the daily generation schedule. The proposed model creates a day ahead forecast of hourly generation curve of the photo-voltaic power plants under consideration with a normalized absolute percentage error 6.5 - 8.4%. Increasing the accuracy of solar power forecasting can positively affect the operation and planning of the central power system of Mongolia. © 2023 IEEE.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85178026169

U2 - 10.1109/BUSSEC59406.2023.10296344

DO - 10.1109/BUSSEC59406.2023.10296344

M3 - Conference contribution

SN - 979-835035807-0

SP - 84

EP - 87

BT - Proceedings of the 2023 Belarusian-Ural-Siberian Smart Energy Conference, BUSSEC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 25 September 2023 through 29 September 2023

ER -

ID: 49265967