基于特征变权的超短期风电功率预测

ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON VARIABLE FEATURE WEIGHT

针对当前风电功率预测过程中历史信息利用不充分及多维输入权重值固定忽略了不同时间维度的特征重要性的问题,提出一种基于特征变权的风电功率预测模型。该方法利用随机森林(RF)分析不同高度处的风速、风向、温度等气象特征对风电输出功率的影响程度,并利用累积贡献率完成气象特征的提取。对提取的特征及历史功率信息利用奇异谱分析(SSA)去噪,以去噪后的数据作为输入建立级联式FA-CNN-LSTM多变量预测模型对超短期风电功率进行预测。通过在CNN-LSTM网络中增加特征注意力机制(FA)自适应挖掘不同时刻的特征关系,动态调整不同时间维度各输入特征的权重,加强预测时刻关键特征的注意力,从而提升预测性能。基于某风电场实测数据的算例分析表明,所提方法可有效提高超短期风电功率预测精度。

Aiming at the problems of insufficient utilization of historical information and the fixed multi-dimensional input weight ignoring the importance of features in different time dimensions in current wind power prediction process,a wind power prediction model based on feature variable weight is proposed. Random forest(RF)is used to analyze the degree of influence of wind speed,wind direction,temperature and other meteorological characteristics at different heights on the wind power and cumulative contribution rate is used to complete the extraction of meteorological features. Singular spectrum analysis(SSA)is used to denoise the extracted features and historical power information,and the denoised data is used as input to establish a cascaded FA-CNN-LSTM multivariate prediction model to predict ultra-short-term wind power. By adding feature attention mechanism(FA)to CNN-LSTM network to adaptively mine feature relationships at different time,the weights of input features at different time dimensions can be dynamically adjusted to enhance the attention of key features at prediction moment,and the prediction performance can be improved. The case study shows that the proposed method can effectively improve the accuracy of ultra-short-term wind power prediction.