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

人工神经网络 伯努利原理 能源消耗 支持向量机 能量(信号处理) 人工智能 计算机科学 非线性系统 工程类 数学 统计 量子力学 电气工程 物理 航空航天工程
作者
Yonghong Zhang,Shuhua Mao,Yuxiao Kang
出处
期刊:Grey systems [Emerald Publishing Limited]
卷期号:11 (4): 571-595 被引量:14
标识
DOI:10.1108/gs-08-2020-0101
摘要

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.
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