小波包分解与多个机器学习模型耦合在风速预报中的对比

准确预报风速是提高风电利用率以及电力系统稳定性的有效方法。学者们提出了大量风速预报模型，但针对不同下垫面不同风速预报模型的对比研究较少。该研究主要探究小波包分解和12个机器学习模型耦合对3种下垫面（戈壁、绿洲和沙漠）风速预报能力，探索风速预报的优化耦合模型。设置3组模型实验进行对比：单一机器学习模型、小波包分解-机器学习混合模型和小波包分解-机器学习-卷积神经网络混合模型。结果表明：具有特征选择和记忆功能的深度学习模型（如卷积长短时记忆网络）以及极限学习机对风速具有较好的预报能力，小波包分解可以显著提高模型精度。小波包分解与卷积长短时记忆网络、卷积门控循环单元和极限学习机的耦合模型在风速预报中具有较好的表现。这表明信号分解和深度学习的耦合模型，能有效提高预报精度，值得推广。

The hybrid of wavelet packet decomposition and machine learning models in wind speed forecasting

Wind speed forecasting is an effective method to improve wind power utilization and power system stability.Previous studies proposed a amount of wind speed forecast models,but there are few comparative studies on wind speed forecast models for different underlying land cover.This study mainly explores the ability of wavelet packet decomposition,12 machine learning models and their coupled models to forecast the wind speed for three different underlying land cover and landforms(gobi,oasis and desert),and explores the optimized coupled model for wind speed forecast.Three sets of model experiments were set up for comparison in the article:single XXX model,WPD-XXX hybrid model and WPD-XXX-CNN hybrid model.We found that the deep learning models with feature selection,memory functions(such as convolutional long short-term memory networks)and extreme learning machine have better forecast capabilities for wind speed forecast.The wavelet packet decomposition significantly improves model accuracy.The coupled modesl of wavelet packet decomposition and convolutional long short-term memory network,convolutional gated recurrent unit network has better performance.This shows that the use of signal decomposition and the coupling of deep learning models can effectively improve the forecast accuracy of the models.It could be widely used in industry practices.