Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity. On the one hand, its output sequence has daily periodicity; on the other hand, it has discrete randomness. With the development of new energy economy, the proportion of photovoltaic energy increased accordingly. In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation, this paper proposes the short-term prediction of photovoltaic power generation based on the improved multi-scale permutation entropy, local mean decomposition and singular spectrum analysis algorithm. Firstly, taking the power output per unit day as the research object, the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions, and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy, sunny, abrupt, cloudy. Then, local mean decomposition (LMD) is used to decompose the output sequence, so as to extract more detail components of the reconstructed output sequence. Finally, combined with the weather forecast of the Meteorological Bureau for the next day, the singular spectrum analysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather. Through the verification and analysis of examples, the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared. The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator, and has the advantages of simple structure and high prediction accuracy. © 2023, Tech Science Press. All rights reserved.