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Article

Interval Forecasting Method of Aggregate Output for Multiple Wind Farms Using LSTM Networks and Time-Varying Regular Vine Copulas

1
School of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
2
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
*
Author to whom correspondence should be addressed.
Processes 2023, 11(5), 1530; https://doi.org/10.3390/pr11051530
Submission received: 30 March 2023 / Revised: 14 May 2023 / Accepted: 15 May 2023 / Published: 17 May 2023

Abstract

Interval forecasting has become a research hotspot in recent years because it provides richer uncertainty information on wind power output than spot forecasting. However, compared with studies on single wind farms, fewer studies exist for multiple wind farms. To determine the aggregate output of multiple wind farms, this paper proposes an interval forecasting method based on long short-term memory (LSTM) networks and copula theory. The method uses LSTM networks for spot forecasting firstly and then uses the forecasting error data generated by LSTM networks to model the conditional joint probability distribution of the forecasting errors for multiple wind farms through the time-varying regular vine copula (TVRVC) model, so as to obtain the probability interval of aggregate output for multiple wind farms under different confidence levels. The proposed method is applied to three adjacent wind farms in Northwest China and the results show that the forecasting intervals generated by the proposed method have high reliability with narrow widths. Moreover, comparing the proposed method with other four methods, the results show that the proposed method has better forecasting performance due to the consideration of the time-varying correlations among multiple wind farms and the use of a spot forecasting model with smaller errors.
Keywords: interval forecast; multiple wind farms; LSTM network; regular vine copulas; time-varying copula interval forecast; multiple wind farms; LSTM network; regular vine copulas; time-varying copula
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MDPI and ACS Style

Wang, Y.; Sun, Y.; Li, Y.; Feng, C.; Chen, P. Interval Forecasting Method of Aggregate Output for Multiple Wind Farms Using LSTM Networks and Time-Varying Regular Vine Copulas. Processes 2023, 11, 1530. https://doi.org/10.3390/pr11051530

AMA Style

Wang Y, Sun Y, Li Y, Feng C, Chen P. Interval Forecasting Method of Aggregate Output for Multiple Wind Farms Using LSTM Networks and Time-Varying Regular Vine Copulas. Processes. 2023; 11(5):1530. https://doi.org/10.3390/pr11051530

Chicago/Turabian Style

Wang, Yanwen, Yanying Sun, Yalong Li, Chen Feng, and Peng Chen. 2023. "Interval Forecasting Method of Aggregate Output for Multiple Wind Farms Using LSTM Networks and Time-Varying Regular Vine Copulas" Processes 11, no. 5: 1530. https://doi.org/10.3390/pr11051530

APA Style

Wang, Y., Sun, Y., Li, Y., Feng, C., & Chen, P. (2023). Interval Forecasting Method of Aggregate Output for Multiple Wind Farms Using LSTM Networks and Time-Varying Regular Vine Copulas. Processes, 11(5), 1530. https://doi.org/10.3390/pr11051530

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