Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: A case of Hubei in China

Wen-Ze Wu, Haodan Pang, Chengli Zheng, Wanli Xie and Chong Liu

Energy, 2021, vol. 229, issue C

Abstract: Accurate electricity consumption forecasting plays a crucial role in electric power systems and is a challenging task due to its complicated mechanism induced by multiple influential factors. To address this problem, this paper develops a novel nonhomogeneous discrete grey model that considers seasonality by introducing a seasonal index into the fractional accumulation generation operator, which is abbreviated as SFNDGM. In addition, the introduction of a one-step ahead rolling mechanism can further improve its prediction performance. To enhance the efficacy, the particle swarm optimization (PSO) algorithm is employed to determine the fractional accumulation order. Subsequently, to demonstrate the effectiveness and superiority of the rolling SFNDGM model, this model is applied to simulate and predict Hubei’s quarterly electricity consumption from 2010Q4 to 2019Q3. The numerical results show that the proposed rolling model has a better performance than the other benchmark models. Therefore, the optimal model is utilized to predict Hubei’s quarterly electricity consumption by 2021, inferring that electricity consumption will continue to increase in the coming years. Based on the attained forecasts, several suggestions are put forward to promote the sustainable development of Hubei’s electricity consumption.

Keywords: Electricity consumption; Seasonal characteristics; Grey modeling technique; Particle swarm optimization (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:229:y:2021:i:c:s0360544221009622

DOI: 10.1016/j.energy.2021.120714

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