Ultra-short-term PV power prediction model based on HP-OVMD and enhanced emotional neural network

Accurate photovoltaic (PV) power prediction plays an increasingly crucial role to maintain the safety and reliability of power grid operation. However, the fluctuation and non-stationarity of PV power make it a challenging task to optimize the accurate results. This paper presents a novel prediction model which is the combination of Hodrick-Prescott (HP) filter, optimized variational mode decomposition (OVMD), and enhanced emotional neural network (EENN). It overcomes the adverse effects of random changes under highly volatile weather conditions. First, the trend component and fluctuation component of PV power are screened through HP filter as the pre-step to alleviate the non-linearity impact of PV power data. Then, OVMD is used to decompose the residual PV power time series into a series of relatively stationary intrinsic modes. Finally, the EENN model optimized by the grey wolf optimization (GWO) is established to predict each subseries, and the prediction results of each subseries are reconstructed to obtain the final predicted results. The numerical results based on actual PV power data show that the prediction accuracy of the proposed model is significantly improved compared with the contrast models, and the proposed model achieves the best accuracy against the OVMD-GWO-EENN, VMD-GWO-EENN, and GWO-EENN models.

Automated summary

This paper proposes a novel prediction model for photovoltaic (PV) power. By combining HP filter, OVMD, and EENN model, it overcomes the challenges posed by the fluctuation and non-stationarity of PV power. Numerical results demonstrate that the proposed model performs significantly better than other models in terms of prediction accuracy.