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Short-Term Wind Power Prediction Based on Improved SAO-Optimized LSTM

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Vydáno v:Processes vol. 13, no. 7 (2025), p. 2192-2209
Hlavní autor: Liu Zuoquan
Další autoři: Liu, Xinyu, Zhang Haocheng
Vydáno:
MDPI AG
Témata:
Accuracy
Distributed generation
Wind power
Lagrange multiplier
Fourier transforms
Forecasting
Prediction models
Integrated energy systems
Neural networks
Ablation
Optimization
Power series
Alternative energy sources
Long short-term memory
Optimization algorithms
Entropy
Decomposition
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Abstrakt:To enhance the accuracy of short-term wind power forecasting, this study proposes a hybrid model combining Northern Goshawk Optimization (NGO)-optimized Variational Mode Decomposition (VMD) and an Improved Snow Ablation Optimizer (ISAO)-optimized Long Short-Term Memory (LSTM) network. Initially, NGO is applied to determine the optimal parameters for VMD, decomposing the original wind power series into multiple frequency-based subsequences. Subsequently, ISAO is employed to fine-tune the hyperparameters of the LSTM, resulting in an ISAO-LSTM prediction model. The final forecast is obtained by reconstructing the subsequences through superposition. Experiments conducted on real data from a wind farm in Ningxia, China demonstrate that the proposed approach significantly outperforms traditional single and combined models, yielding predictions that closely align with actual measurements. This validates the method’s effectiveness for short-term wind power prediction and offers valuable data support for optimizing microgrid scheduling and capacity planning in wind-integrated energy systems.
ISSN:2227-9717
DOI:10.3390/pr13072192
Zdroj:Materials Science Database