Very short-term probabilistic prediction of PV based on multi-period error distribution

Abstract

• Empirical mode decomposition based on long short-term memory and backpropagation neural network (EEMD-LSTM-BP) is proposed. • EEMD is utilized to study the characteristic of wave behaviors in different frequency domains. • LSTM and BP are used to determine intrinsic mode functions (IMFs) and remaining components. • Based on the dataset of PV stations in Ningxia Province, the case studies verify the method's feasibility. As the penetration rate of photovoltaic (PV) in the grid increases, enormous challenges have been brought into power grid dispatcher's operation. Efficient and accurate PV power prediction is the key to solve this problem. Considering multi-period error distribution (MPED), a novel probabilistic prediction approach via ensemble empirical mode decomposition based on long short-term memory and backpropagation neural network (EEMD-LSTM-BP) is proposed. EEMD is utilized to study the characteristic of wave behaviors in different frequency domains. LSTM and BP are used to determine intrinsic mode functions (IMFs) and remaining components, respectively. Afterward, based on the prediction errors, PV power output fluctuation in different periods is analysed. The segment points are determined by Nadaraya-Watson (N-W) kernel regression. The bounds of prediction intervals (PIs) are quantified based on the error probability distribution. Based on the dataset of PV stations in Ningxia Province, the case studies verify the method's feasibility.