Performance Improvement of Photovoltaic Power Forecasting Model Based on Hilbert Huang Transforsm

Abstract

Photovoltaic power generation is fluctuation and intermittent, and the grid-connected operation of large-scale photovoltaic power plants may have an impact on the safe and stable economic operation of the power system. An effective way to solve the problem is to make scientific forecasts of the output power of PV power plants. In this paper, Back Propagation (BP) and Radial Basis Function (RBF) neural network prediction models are established by using the historical values of actual power generation of Guhe Runneng Photovoltaic Power Station in Gaotang County of Liaocheng City in 2017. According to the characteristics of photovoltaic power fluctuation, the output power is treated as a set of digital signals for short-term PV power prediction, and a prediction model based on the Hilbert Huang Transform (HHT) power data decomposition is proposed. Through the analysis of an example, it can be concluded that after HHT, the prediction effect is significantly improved, and the accuracy of PV power prediction is improved. Moreover, compared with BP neural network, RBF neural network has smaller prediction error.