An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target

• Published in: Sustainability 2021-08, Vol.13 (15), p.8670
• Main Authors: Cui, Xiwen, E, Shaojun, Niu, Dongxiao, Wang, Dongyu, Li, Mingyu
• Format: Article
• Language: English
• Subjects: Accuracy; Alternative energy sources; Artificial intelligence; Carbon; Development policy; Economic development; Energy consumption; Forecasting; Mathematical models; Neural networks; Optimization; Optimization algorithms; Pollution; Support vector machines; Sustainability; Sustainable development
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su13158670

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.