Standard

Short-Term Wind Power Forecasting Based on Gaussian Process Regression. / Snegirev, D. A.; Pazderin, A. V.; Samoylenko, V. O. et al.
2023 6th International Scientific and Technical Conference Relay Protection and Automation, RPA 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1-13.

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

Harvard

Snegirev, DA, Pazderin, AV, Samoylenko, VO & Berdin, AS 2023, Short-Term Wind Power Forecasting Based on Gaussian Process Regression. in 2023 6th International Scientific and Technical Conference Relay Protection and Automation, RPA 2023: book. Institute of Electrical and Electronics Engineers Inc., pp. 1-13, 2023 6th International Scientific and Technical Conference on Relay Protection and Automation (RPA), 18/10/2023. https://doi.org/10.1109/RPA59835.2023.10319865

APA

Snegirev, D. A., Pazderin, A. V., Samoylenko, V. O., & Berdin, A. S. (2023). Short-Term Wind Power Forecasting Based on Gaussian Process Regression. In 2023 6th International Scientific and Technical Conference Relay Protection and Automation, RPA 2023: book (pp. 1-13). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RPA59835.2023.10319865

Vancouver

Snegirev DA, Pazderin AV, Samoylenko VO, Berdin AS. Short-Term Wind Power Forecasting Based on Gaussian Process Regression. In 2023 6th International Scientific and Technical Conference Relay Protection and Automation, RPA 2023: book. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1-13 doi: 10.1109/RPA59835.2023.10319865

Author

Snegirev, D. A. ; Pazderin, A. V. ; Samoylenko, V. O. et al. / Short-Term Wind Power Forecasting Based on Gaussian Process Regression. 2023 6th International Scientific and Technical Conference Relay Protection and Automation, RPA 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1-13

BibTeX

@inproceedings{aa60d0350d7a48eba1cf3de13e0b807b,
title = "Short-Term Wind Power Forecasting Based on Gaussian Process Regression",
abstract = "The growing share of renewable energy sources with stochastic nature generation in the powers systems introduces new challenges for short-term and ultra-short-term power systems balances planning. A significant portion of such generation is consisting of wind power plants. Wind power forecasting is one of the most effective and least capital-intensive for wind farms integration to power systems. This paper presents a technique for short-term wind power plants generation forecasting using a regression model based on Gaussian processes.",
author = "Snegirev, {D. A.} and Pazderin, {A. V.} and Samoylenko, {V. O.} and Berdin, {A. S.}",
year = "2023",
month = oct,
day = "18",
doi = "10.1109/RPA59835.2023.10319865",
language = "English",
isbn = "979-835038233-4",
pages = "1--13",
booktitle = "2023 6th International Scientific and Technical Conference Relay Protection and Automation, RPA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2023 6th International Scientific and Technical Conference on Relay Protection and Automation (RPA) ; Conference date: 18-10-2023 Through 20-10-2023",

}

RIS

TY - GEN

T1 - Short-Term Wind Power Forecasting Based on Gaussian Process Regression

AU - Snegirev, D. A.

AU - Pazderin, A. V.

AU - Samoylenko, V. O.

AU - Berdin, A. S.

PY - 2023/10/18

Y1 - 2023/10/18

N2 - The growing share of renewable energy sources with stochastic nature generation in the powers systems introduces new challenges for short-term and ultra-short-term power systems balances planning. A significant portion of such generation is consisting of wind power plants. Wind power forecasting is one of the most effective and least capital-intensive for wind farms integration to power systems. This paper presents a technique for short-term wind power plants generation forecasting using a regression model based on Gaussian processes.

AB - The growing share of renewable energy sources with stochastic nature generation in the powers systems introduces new challenges for short-term and ultra-short-term power systems balances planning. A significant portion of such generation is consisting of wind power plants. Wind power forecasting is one of the most effective and least capital-intensive for wind farms integration to power systems. This paper presents a technique for short-term wind power plants generation forecasting using a regression model based on Gaussian processes.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85179896038

U2 - 10.1109/RPA59835.2023.10319865

DO - 10.1109/RPA59835.2023.10319865

M3 - Conference contribution

SN - 979-835038233-4

SP - 1

EP - 13

BT - 2023 6th International Scientific and Technical Conference Relay Protection and Automation, RPA 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 6th International Scientific and Technical Conference on Relay Protection and Automation (RPA)

Y2 - 18 October 2023 through 20 October 2023

ER -

ID: 49811040