Prediction of Whole Social Electricity Consumption in Jiangsu Province Based on Metabolic FGM (1, 1) Model

The achievement of the carbon peaking and carbon neutrality targets requires the adjustment of the energy structure, in which the dual-carbon progress of the power industry will directly affect the realization process of the goal. In such terms, an accurate demand forecast is imperative for the gove...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2022-06, Vol.10 (11), p.1791
Main Authors: Zhang, Siyu, Wu, Liusan, Cheng, Ming, Zhang, Dongqing
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Carbon
Decision making
Econometrics
Economic development
Economic forecasting
Electricity
Electricity consumption
Emissions
Energy consumption
Energy industry
Fuzzy logic
Fuzzy sets
metabolic FGM (1, 1) model
Metabolism
Methods
Neural networks
Optimization algorithms
Parameter estimation
Particle swarm optimization
PSO algorithm
Time series
Trends
Values
whole social electricity consumption
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The achievement of the carbon peaking and carbon neutrality targets requires the adjustment of the energy structure, in which the dual-carbon progress of the power industry will directly affect the realization process of the goal. In such terms, an accurate demand forecast is imperative for the government and enterprises’ decision makers to develop an optimal strategy for electric energy planning work in advance. According to the data of the whole social electricity consumption in Jiangsu Province of China from 2015 to 2019, this paper uses the improved particle swarm optimization algorithm to calculate the fractional-order r of the FGM (1, 1) model and establishes a metabolic FGM (1, 1) model to predict the whole social electricity consumption in Jiangsu Province of China from 2020 to 2023. The results show that in the next few years the whole social electricity consumption in Jiangsu Province will show a growth trend, but the growth rate will slow down generally. It can be seen that the prediction accuracy of the metabolic FGM (1, 1) model is higher than that of the GM (1, 1) and FGM (1, 1) models. In addition, the paper analyzes the reasons for the changes in the whole society electricity consumption in Jiangsu Province of China and provides support for government decision making.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10111791