Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model

Wenqing Wu, Xin Ma, Bo Zeng, Yong Wang and Wei Cai

Renewable Energy, 2019, vol. 140, issue C, 70-87

Abstract: Energy consumption is an international issue and plays an important role in the national energy security, especially for China of which the energy market is in transition. Accuracy and trustable forecasting of future energy consumption trends with nonlinear data sequences is very important for the decision makers of governments and energy companies. In this paper, a novel nonlinear grey Bernoulli model with fractional order accumulation, abbreviated as FANGBM(1,1) model, is proposed to forecast short-term renewable energy consumption of China during the 13th Five-Year Plan (2016–2020). The new model is discussed in details with the fractional accumulated generating matrix and the Bernoulli equation. Further, the Particle Swarm Optimization algorithm is used to search optimal system parameters. Based on the updated real-world data sets from 2011 to 2015, the FANGBM(1,1) model is established to forecast the total renewable energy consumption, hydroelectricity consumption, wind consumption, solar consumption, and consumption of other renewable energies, respectively. The FANGBM(1,1) model presents high accuracy in all cases and is also proved to be efficient to deal with nonlinear sequences.

Keywords: Renewable energy consumption; Grey Bernoulli model; Fractional order accumulation; FANGBM(1,1) model; Particle swarm optimization; Five-Year Plan (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (60)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:140:y:2019:i:c:p:70-87

DOI: 10.1016/j.renene.2019.03.006

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