基于自适应组合模型的超短期风速预测

风电场的风速预测对电力系统的稳定及安全运行有着重大的影响.考虑到风速序列具有间歇性和随机性等特征,提出一种基于参数优化的变分模态分解及极限学习机的组合模型,将其用于超短期风速预测.首先,采用变分模态分解算法将风速序列分解为一系列的平稳分量.以正交性为适应度函数,利用网格优化算法搜索变分模态分解的关键参数值——分解层数和...

Ultra-short-term wind speed prediction based on an adaptive integrated model

Wind speed prediction has a significant impact on the stable and safe operation of a power system.According to the intermittent and random nature of wind speed,an integrated model of variational modal decomposition(VMD)based on grid search optimization algorithm(GS)and PSO-ELM is proposed for ultra-short-term wind speed prediction.First,the VMD is used to decompose wind speed sequence into a series of sub-sequences.By taking the orthogonality as the fitness function,the GS is used to search the key parameters of VMD adaptively,including the number of decomposed layers and a penalty factor.The purpose is to ensure information orthogonality between each component and to suppress coupling components.Then,the extreme learning machine(ELM)method is used to predict each sub-sequence.Given the unstable prediction of this model,particle swarm algorithm(PSO)is used to optimize the parameters of the initial weight and threshold,and the input dimension of each sub-sequence is determined adaptively by using the auto-regressive integrated moving average model(ARIMA).Finally,the predicted results of each sub-sequence are superimposed to obtain the final predicted wind speed.The result shows that the proposed integrated model is remarkably superior to all considered benchmark models in prediction accuracy.