A Wind Power Forecasting Model Using LSTM Optimized by the Modified Bald Eagle Search Algorithm

Wumaier Tuerxun, Chang Xu, Hongyu Guo, Lei Guo, Namei Zeng and Yansong Gao
Additional contact information
Wumaier Tuerxun: College of Water Conservancy and Hydro-Power Engineering, HoHai University, Nanjing 210098, China
Chang Xu: College of Energy and Electrical Engineering, HoHai University, Nanjing 210098, China
Hongyu Guo: College of Energy and Electrical Engineering, HoHai University, Nanjing 210098, China
Lei Guo: College of Water Conservancy and Hydro-Power Engineering, HoHai University, Nanjing 210098, China
Namei Zeng: Clean Energy Branch of Huaneng International Power Jiangsu Energy Development Co., Nanjing 210098, China
Yansong Gao: College of Energy and Electrical Engineering, HoHai University, Nanjing 210098, China

Energies, 2022, vol. 15, issue 6, 1-19

Abstract: High-precision forecasting of short-term wind power (WP) is integral for wind farms, the safe dispatch of power systems, and the stable operation of the power grid. Currently, the data related to the operation and maintenance of wind farms mainly comes from the Supervisory Control and Data Acquisition (SCADA) systems, with certain information about the operating characteristics of wind turbines being readable in the SCADA data. In short-term WP forecasting, Long Short-Term Memory (LSTM) is a commonly used in-depth learning method. In the present study, an optimized LSTM based on the modified bald eagle search (MBES) algorithm was established to construct an MBES-LSTM model, a short-term WP forecasting model to make predictions, so as to address the problem that the selection of LSTM hyperparameters may affect the forecasting results. After preprocessing the WP data acquired by SCADA, the MBES-LSTM model was used to forecast the WP. The experimental results reveal that, compared with the PSO-RBF, PSO-SVM, LSTM, PSO-LSTM, and BES-LSTM forecasting models, the MBES-LSTM model could effectively improve the accuracy of WP forecasting for wind farms.

Keywords: MBES algorithm; WP forecasting; LSTM; wind turbine; parameter optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/6/2031/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/6/2031/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:6:p:2031-:d:768337

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().