Short-term photovoltaic power prediction on modal reconstruction: A novel hybrid model approach

Abstract

The contribution is to propose a novel hybrid model based on modal reconstruction to predict short-term photovoltaic (PV) power. PV power generation large-scale grid connection causes the impact on the power system due to the instability and intermittence, and PV curtailment measures are taken to reduce the impact from voltage fluctuation. Accurate forecast is necessary to make a reasonable generation plan. A novel hybrid forecasting model is proposed, and an enhanced gray wolf optimization algorithm is proposed to improve the convergence ability and to solve the influence of extreme learning machine random parameters. The proposed algorithm has stronger convergence stability and higher convergence accuracy compared with the existing algorithms. The ensemble empirical mode decomposition algorithm is used to decompose the PV power under different weather conditions. The complexity of each component is calculated by the sample entropy, and the components are reconstructed to reduce the computational cost of forecasting models. The results revealed that mean absolute percentage error and root mean square error of the proposed model are smaller than 3% and 0.36 under various weather conditions. Meanwhile, the determination coefficient of the proposed model is more than 98%.