Short-term prediction of photovoltaic power generation based on LMD⁃EE⁃ESN with error correction

Considering the instability of the output power of photovoltaic(PV) generation system, to improve the power regulation ability of PV power during grid-connected operation, based on the quantitative analysis of meteorological conditions, a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed. Firstly, through the fuzzy clustering processing of meteorological conditions, taking the power curves of PV power generation in sunny, rainy or snowy, cloudy, and changeable weather as the reference, the local mean decomposition(LMD) was carried out respectively, and their energy entropy (EE) was taken as the meteorological characteristics. Then, the historical generation power series was decomposed by LMD algorithm, and the hierarchical prediction of the power curve was realized by echo state network(ESN) prediction algorithm combined with meteorological characteristics. Finally, the iterative error theory was applied to the correction of power prediction results. The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power, and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.