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

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.