基于K-SVD-OMP和KELM组合方法的短期光伏功率预测

Short-term photovoltaic power prediction using combined K-SVD-OMP and KELM method

针对光伏功率预测,提出一种基于稀疏表示的特征提取建模方法。首先,将所有影响光伏功率预测输出的因素作为模型的输入,将模型输入构成的数据向量作为初始字典,由K-均值奇异值分解(K-mean singular value decomposition,K-SVD)算法进行稀疏分解与变换,得到学习后的字典。其次,由正交匹配追踪(Orthogonal matching pursuit,OMP)算法获取相应的稀疏编码向量,再将其作为核极限学习机(Kernel extreme learning machine,KELM)的模型输入,以构建全局回归模型。为了验证K-SVD-OMP和KELM组合方法的有效性,将所提出的方法应用于光伏功率预测实例中,在相同条件下,与KELM、SVM、ELM等方法进行了比较。实验结果表明,不同的稀疏表示建模组合方法均可以达到好的预测效果,其中K-SVDOMP和KELM的组合方法可以给出更好的预测结果与精度。

For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.