Using a self-adaptive grey fractional weighted model to forecast Jiangsu's electricity consumption in China. (1st January 2020)

• Record Type: Journal Article
• Authors: Zhu, Xiaoyue; Dang, Yaoguo; Ding, Song
• Is Part Of: Energy. Volume 190(2020)
• Publication Date: 2020-01-01
• Subjects: Grey system -- Grey prediction model -- Novel initial condition -- Fractional grey model -- Electricity consumption prediction; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116417
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The remarkable prediction performance of electricity consumption has always assumed particular importance for electric power utility planning and economic development. On account of the complexity and uncertainty of the electricity system, this paper establishes a self-adaptive grey fractional weighted model to predict Jiangsu's electricity consumption, which efficiently enhances the prediction quality of electricity consumption. This newly constructed grey model introduces the fractional weighted coefficients to design a novel initial condition. Compared with the old one in the conventional grey models, the newly optimized initial condition has a flexible structure, which has advantages in capturing the dynamic characteristics of the electricity consumption observations. In addition, to further promote the forecasting precision, the adjustable fractional weighted coefficients and corresponding time parameter of the initial condition are estimated by utilizing the Particle Swarm Algorithm (PSO). Furthermore, five competing models are employed to forecast Jiangsu's electricity consumption in China, which certifies the validity of the established model. Experimental results illustrate that the newly designed model has significant advantages over other five competing models. According to the forecasted results, electricity consumption in Jiangsu Province is expected to reach 6778 billion kilowatt-hours in 2020, while the growth rate will fall down by 1.11%. Therefore, several proposals are made for decision-makers. Highlights: A self-adaptive fractional weighted grey model is developed for electricity demand forecast. The novel initial condition having dynamic fractional order weighted coefficients is proposed. The PSO algorithm is applied to estimate the weighted coefficients and time parameter. Jiangsu's electricity consumption is predicted from 2018 to 2020.