电力需求侧管理2025，Vol.27Issue(5)：16-22,7.DOI:10.3969/j.issn.1009-1831.2025.05.003
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
摘要
Abstract
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.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Key words
wind speed forecasting/discrete wavelet transform/bidirectional long short-term memory network/bidirectional gated recur-rent unit/non-stationary time series分类
信息技术与安全科学引用本文复制引用
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):16-22,7.基金项目
中国南方电网有限责任公司科技项目(GZKJXM20240079) （GZKJXM20240079）
