A new hybrid model for photovoltaic output power prediction.

Abstract

Recently, with the development of renewable energy technologies, photovoltaic (PV) power generation is widely used in the grid. However, as PV power generation is influenced by external factors, such as solar radiation fluctuation, PV output power is intermittent and volatile, and thus the accurate PV output power prediction is imperative for the grid stability. To address this issue, based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), improved artificial rabbits optimization (IARO) and convolutional bidirectional long short-term memory (CBiLSTM), a new hybrid model denoted by CEEMDAN-IARO-CBiLSTM is proposed. In addition, inputs of the proposed model are optimized by analyzing influential factors of PV output power with Pearson correlation coefficient method. In order to verify the prediction accuracy, CEEMDAN-IARO-CBiLSTM is compared with other well-known methods under different weather conditions and different seasons. Specifically, for different weather conditions, MAE and RMSE of the proposed model decrease by at least 0.329 and 0.411, 0.086 and 0.021, and 0.140 and 0.220, respectively. With respect to different seasons, MAE and RMSE of the proposed model decrease by at least 0.270 and 0.378, 0.158 and 0.209, 0.210 and 0.292, and 1.096 and 1.148, respectively. Moreover, two statistical tests are conducted, and the corresponding results show that the prediction performance of CEEMDAN-IARO-CBiLSTM is superior to other well-known methods.