An integrated framework of gated recurrent unit based on improved sine cosine algorithm for photovoltaic power forecasting. (1st October 2022) - Ma, Huixin; Zhang, Chu; Peng, Tian; Nazir, Muhammad Shahzad; Li, Yiman
Abstract: Accurate prediction of photovoltaic power is of great significance to the storage and utilization of solar power. In this research, a deep learning model for photovoltaic power prediction based on gated recurrent unit network (GRU), improved sine cosine algorithm (ISCA), and complete ensemble empirical mode decomposition (CEEMD) is proposed. Firstly, CEEMD is used to decompose the original photovoltaic data into several intrinsic mode function (IMF) components and one residual. Secondly, each sub-pattern after decomposition is processed by partial least-squares analysis (PLS). Third, the nonlinear strategy is used to improve SCA, and the Hill-climbing strategy is added to the local search part to improve the performance of the algorithm. Fourth, each sub-pattern is predicted by GRU, then the learning rate and the number of hidden layer neurons of GRU are optimized by the ISCA. Finally, the predicted results of each sub-model are combined to generate the final prediction results. In this study, the proposed model is applied to four photovoltaic power data sets, and different experimental comparison models are established. The experimental results show that the CEEMD-PLS-ISCA-GRU model in this study can obtain good prediction results in all data sets. Graphical abstract: Image 1 Highlights: A novel PV power forecasting method is proposed based on GRU. An improved SCA algorithm is proposed. CEEMD is used to decompose the original data of different months. The proposed hybrid model accurately predicts the PV power.