A clean energy forecasting model based on artificial intelligence and fractional derivative grey Bernoulli models

Purpose With the massive use of fossil energy polluting the natural environment, clean energy has gradually become the focus of future energy development. The purpose of this article is to propose a new hybrid forecasting model to forecast the production and consumption of clean energy. Design/methodology/approach Firstly, the memory characteristics of the production and consumption of clean energy were analyzed by the rescaled range analysis (R/S) method. Secondly, the original series was decomposed into several components and residuals with different characteristics by the ensemble empirical mode decomposition (EEMD) algorithm, and the residuals were predicted by the fractional derivative grey Bernoulli model [FDGBM ( p , 1)]. The other components were predicted using artificial intelligence (AI) models (least square support vector regression [LSSVR] and artificial neural network [ANN]). Finally, the fitting values of each part were added to get the predicted value of the original series. Findings This study found that clean energy had memory characteristics. The hybrid models EEMD–FDGBM ( p , 1)–LSSVR and EEMD–FDGBM ( p , 1)–ANN were significantly higher than other models in the prediction of clean energy production and consumption. Originality/value Consider that clean energy has complex nonlinear and memory characteristics. In this paper, the EEMD method combined the FDGBM ( P , 1) and AI models to establish hybrid models to predict the consumption and output of clean energy.