Spatio-temporal modeling with enhanced flexibility and robustness of solar irradiance prediction: A chain-structure echo state network approach

In this paper, a chain-structure echo state network (CESN) is newly proposed to enhance the scalability, robustness and computational efficiency in spatio-temporal solar irradiance prediction. Firstly, the network structure, mathematical model, stability analysis, and training mechanism are studied for CESN. Secondly, according to the spatial correlation analysis, the number of ESN modules in CESN can be determined, such that the dynamics of different features can be fully fitted. In addition, the autocorrelation analysis is adopted in temporal information of each spatial variable to provide appropriate inputs for each ESN module. Finally, the spatio-temporal solar irradiance prediction model is established based on CESN. Simulation results illustrate that the CESN could achieve more accurate prediction, compared with backpropagation (BP) and Elman neural networks, classical ESNs. (C) 2020 Elsevier Ltd. All rights reserved.