Short-Term Wind Power Prediction Based on Improved SAO-Optimized LSTM
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| Publicado en: | Processes vol. 13, no. 7 (2025), p. 2192-2209 |
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| Autor principal: | |
| Otros Autores: | , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2227-9717 | ||
| 024 | 7 | |a 10.3390/pr13072192 |2 doi | |
| 035 | |a 3233242230 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Liu Zuoquan |u Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China | |
| 245 | 1 | |a Short-Term Wind Power Prediction Based on Improved SAO-Optimized LSTM | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Accuracy | ||
| 653 | |a Distributed generation | ||
| 653 | |a Wind power | ||
| 653 | |a Lagrange multiplier | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Forecasting | ||
| 653 | |a Prediction models | ||
| 653 | |a Integrated energy systems | ||
| 653 | |a Neural networks | ||
| 653 | |a Ablation | ||
| 653 | |a Optimization | ||
| 653 | |a Power series | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Long short-term memory | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Entropy | ||
| 653 | |a Decomposition | ||
| 700 | 1 | |a Liu, Xinyu |u School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China | |
| 700 | 1 | |a Zhang Haocheng |u School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China | |
| 773 | 0 | |t Processes |g vol. 13, no. 7 (2025), p. 2192-2209 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233242230/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233242230/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233242230/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |