A Combined Forecasting Model Based on a Modified Pelican Optimization Algorithm for Ultra-Short-Term Wind Speed

Ultra-short-term wind speed forecasting is crucial for ensuring the safe grid integration of wind energy and promoting the efficient utilization and sustainable development of renewable energy sources. However, due to the arbitrary, intermittent, and volatile nature of wind speed, achieving satisfac...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Sustainability Ročník 17; číslo 5; s. 2081
Hlavní autori: Guo, Lei, Xu, Chang, Ai, Xin, Han, Xingxing, Xue, Feifei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.03.2025
Predmet:
Accuracy
Algorithms
Alternative energy sources
Artificial intelligence
Carbon
Mathematical optimization
Neural networks
Optimization algorithms
Parameter estimation
Renewable resources
Speed
Weather forecasting
Wind farms
Wind power
Winds
China
ISSN:2071-1050, 2071-1050
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Ultra-short-term wind speed forecasting is crucial for ensuring the safe grid integration of wind energy and promoting the efficient utilization and sustainable development of renewable energy sources. However, due to the arbitrary, intermittent, and volatile nature of wind speed, achieving satisfactory forecasts is challenging. This paper proposes a combined forecasting model using a modified pelican optimization algorithm, variational mode decomposition, and long short-term memory. To address issues in the current combination model, such as poor optimization and convergence performance, the pelican optimization algorithm is improved by incorporating tent map-based population initialization, Lévy flight strategy, and classification optimization concepts. Additionally, to obtain the optimal parameter combination, the modified pelican optimization algorithm is used to optimize the parameters of variational mode decomposition and long short-term memory, further enhancing the model’s predictive accuracy and stability. Wind speed data from a wind farm in China are used for prediction, and the proposed combined model is evaluated using six indicators. Compared to the best model among all compared models, the proposed model shows a 10.05% decrease in MAE, 4.62% decrease in RMSE, 17.43% decrease in MAPE, and a 0.22% increase in R2. The results demonstrate that the proposed model has better accuracy and stability, making it effective for wind speed prediction in wind farms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2071-1050
2071-1050
DOI:10.3390/su17052081