Short-term prediction of photovoltaic power generation based on ICEEMDAN-SE-GAPSO-LSTM

Abstract

This study proposes an improved adaptive noise-complete ensemble empirical mode decomposition (ICEEMDAN), sample entropy (SE), and genetic particle swarm optimization (GAPSO) algorithm-based short-term solar power forecast model. The photovoltaic power data is first decomposed using the ICEEMDAN algorithm to produce a number of eigenmode components with various properties. The sample entropy of each component is then calculated, and the components with similar sample entropy values are combined to create new modal components. These new modal components are then input into the GAPSO-optimized LSTM model for prediction, and the prediction results of each component are summed and reconstructed to produce the final prediction. The results completely validate the efficacy and dependability of the suggested method, using the measured data from a photovoltaic power station in Xinjiang, China as an example.