2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 16 , Issue 2 , PP: 102-116, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Qian Chen 1 *
Doi: https://doi.org/10.54216/JISIoT.160208
With the acceleration of modern urbanisation, the demand for energy by the state and society is increasing. In order to maintain the sustainable availability of energy, it is necessary to establish an energy sustainability indicator system. To address this issue, this paper proposes an innovative evaluation method for energy sustainability indicator system, which aims to provide a multi-scale and more comprehensive assessment of energy sustainability indicators, as well as to ensure the accuracy and reliability of the evaluation results. This paper proposes to use genetic algorithm and local support vector regression (SGA-LSVM) to optimise the projective fuzzy clustering solution model (PPFCM), to establish a new evaluation method of energy sustainability index system based on genetic algorithm and local support vector regression. Based on this method, energy sustainability in different regions is analyzed according to three indicators: energy supply side, demand side and affordability, and the validity of this evaluation method is tested. The study found that, in terms of zoning: the eastern region is in the lead in energy demand side, energy supply side and energy affordability, and the western region has a rising trend in recent years; in terms of population density: the indices of energy demand side, energy supply side and energy affordability of densely populated areas are much higher than the rest of the areas compared to the sparsely populated areas, and the difference between the indices of energy supply side and energy affordability of the sparsely populated and moderately populated areas and the difference between the indices is not significant. The energy supply-side index is slightly higher than that of the medium-population area; Economy and carbon emission: due to China's focus on environmental protection, carbon emission is kept within a stable range while the economy is developing rapidly. By PC≥0.80, PE≥0.45 and XB≤0.1, it shows that the method of evaluating the energy sustainable development index system using the fuzzy projection-seeking clustering energy sustainable development evaluation model based on genetic algorithm and local vector regression optimization is reliable.
Genetic algorithm , Local support vector regression , Projection fuzzy clustering solution model , Energy sustainable development index system
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