A novel grey prediction model with system structure based on energy background: A case study of Chinese electricity. (1st March 2023)

• Record Type: Journal Article
• Authors: Duan, Huiming; Pang, Xinyu
• Is Part Of: Journal of cleaner production. Volume 390(2023)
• Publication Date: 2023-03-01
• Subjects: Energy consumption -- System structure -- Grey prediction model -- Power consumption -- Forecasting; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2023.136099
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Under the trend of global low-carbon development, reasonable and accurate prediction of electricity consumption plays an essential role in vigorously adjusting power system structure, promoting electrification, and other energy-saving and emission reduction measures. Considering the development trend of energy consumption, this paper introduces the Logistic model of energy structure into the system structure, and establishes a novel grey prediction model with system structure. According to the division of energy factors with similar attributes, this model seeks the internal relationship of the development of electricity consumption and describes the interaction between related factors and multiple main factors in the form of equations, which makes the model have better applicability and stability. In the validation part, the ten types of energy are divided according to their attributes, and the main factor group and the related factor group are distinguished. The model proposed in this paper is used for simulation and prediction, and is compared with the three types of models (six models). In the two cases, the simulation error of the new model is as low as 3.9790%, and the prediction error is 0.5645%. Compared with other models, the new model has shown good performance in the case of electricity consumption forecasting in China. The effectiveness of the optimization of the model in structure, background, and application is verified. At the same time, based on the analysis and prediction of China's consumption data, this paper gives relevant policy suggestions for developing China's power structure. Highlights: A novel model based on energy background (EPMGM(1, M, N)) is established. The relationship of factors in the system are characterized by the equations. A Logistic model based on energy structure is introduced to the new model. China's electricity consumption is effectively predicted by the model.