| 张 俨,王融融,刘佳林,苏华英,邓佳莉,王 寅,王榆楗,郭 炜,付震宇.基于小波变换与双向神经网络的短期风速预测模型[J].电力需求侧管理,2025,27(5):16-22 |
| 基于小波变换与双向神经网络的短期风速预测模型 |
| Short-term wind speed prediction model based on wavelet transform and bidirectional neural networks |
| 投稿时间：2025-05-21 修订日期：2025-06-24 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 05. 003 |
| 中文关键词: 风速预测 离散小波变换 双向长短期记忆网络 双向门控循环单元 非平稳时间序列 |
| 英文关键词: wind speed forecasting discrete wavelet transform bidirectional long short-term memory network bidirectional gated recurrent unit non-stationary time series |
| 基金项目:中国南方电网有限责任公司科技项目（GZKJXM20240079） |
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| 中文摘要: |
| 针对风速数据的非平稳特性导致的预测困难问题，提出一种基于离散小波变换与双向循环神经网络的混合预测模型。模型采用3阶段架构：首先利用离散小波变换将非平稳风速序列分解为多个不同频率的子频带，实现多尺度特征提取；然后将各子频带分别输入双向长短期记忆网络和双向门控循环单元进行并行处理，充分挖掘长短期时序依赖关系；最后通过元学习器对所有子模型的预测结果进行智能融合，生成最终风速预测值。基于西班牙Sotaventogalicia风电场实测数据验证，所提模型在所有评估指标上均显著优于传统方法和现有离散小波变换组合模型，数据挖掘（data mining，DM）统计检验证实了性能提升的显著性，为风速预测提供了高精度的解决方案。 |
| 英文摘要: |
| A hybrid wind speed forecasting model based on discrete wavelet transform（DWT）and bidirectional recurrent neural networks to address the prediction challenges caused by the non-stationary characteristics of wind speed data is proposed. The model employs a three-stage architecture：first, DWT decomposes the non-stationary wind speed sequences into multiple frequency sub-bands to extract multi-scale features；second, each sub-band is fed into bidirectional long short-term memory networks（BiLSTM）and bidirectional gated recurrent units（BiGRU）for parallel processing to fully capture long-term and short-term temporal dependencies；finally, a meta-learner intelligently fuses all sub-model predictions to generate the final wind speed forecast. Experiments on real data from the Sotaventogalicia wind farm in Spain demonstrate that the proposed model significantly outperforms traditional methods and existing DWT-based models across all evaluation metrics. The DM statistical test confirms the statistical significance of the performance improvement, indicating that this hybrid model provides a high-accuracy solution for wind speed forecasting. |
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