Author:
Yuan Jianhua,Gao Yanling,Xie Binbin,Li Hongqiang,Jiang Wenjun
Abstract
Aiming at the problem of high fluctuation and instability of photovoltaic power, a photovoltaic power prediction method combining two techniques has been proposed in this study. In this method, the fast correlation filtering algorithm has been used to extract the meteorological features having a strong correlation with photovoltaic power generation. The complete ensemble empirical mode decomposition with an adaptive noise model has been used to decompose the data into high and low-frequency components to reduce the data volatility. Then, the long short-term neural network and the deep confidence network were combined into a new prediction model to predict each component. Finally, the proposed combined photovoltaic power prediction method has been analyzed using an example and compared with the other prediction methods. The results show that the proposed combined prediction method has higher prediction accuracy.
Subject
Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering