Forecasting the renewable energy consumption of Australia by a novel grey model with conformable fractional opposite-direction accumulation
Related Experiment Video
Updated: Jul 16, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
1Resource Environment and Regional Economic Research Center, Xi'an University of Finance and Economics, Xi'an, 710100, China. wanghuiping@xaufe.edu.cn.
Abstract:
The accurate prediction of renewable energy consumption (REC) is of great significance to ensure energy security, reduce dependence on fossil energy, and promote sustainable economic and social development. In this paper, a novel grey model with conformable fractional opposite-direction accumulation (CFOA), abbreviated as the CFOGM (1,1) model, is proposed to forecast REC in Australia. The new model is discussed in detail with a new CFOA operation and the GM (1,1) model and can take full advantage of the information carried by the original data. The CFOGM (1,1) model has lower modeling error and better fitting and forecasting accuracy than other grey, Holt, and ARM models and can better capture the change trend of REC and achieve accurate prediction. The forecasting results present that the REC in Australia is 497-581 petajoules in 2021, 596-728 petajoules in 2022, and 715-912 petajoules in 2023, indicating that the REC in Australia is still accelerating.
Related Concept Videos
Energy Budgets
Maxwell-Boltzmann Distribution: Problem Solving
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Energy Conservation and Bernoulli's Equation
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Estimation of the Physical Quantities
Fast Decoupled and DC Powerflow

