Scholarlink

<p>Accurate prediction of power demand patterns plays a fundamental role in optimizing electrical distribution infrastructure, enhancing operational efficiency within the energy sector, and supporting sustainable socioeconomic growth across geographical regions. Accurate forecasting provides critical guidance for policymakers and industry stakeholders. In the context of the accelerating shift towards electric energy, analyzing historical consumption trends and accurately predicting future demands is essential. This study introduces a novel PSO−optimized FGM(1,1) model to enhance forecasting accuracy. Applying this model, the study forecasts electricity consumption across 31 regions in China for the period 2025−2035. Furthermore, grey relational analysis (GRA) is utilized to assess the interdependence between electrical energy utilization and carbon dioxide emissions (CO<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10668_2025_6583_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(_2\)</EquationSource> </InlineEquation>), offering valuable insights for optimizing grid planning and supporting sustainable energy policies. The findings contribute practical value to energy distribution planning, carbon management, and environmental policy development.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Modelling relationship among electricity consumption and carbon dioxide emissions in China: A predictive analysis

  • 期刊论文

  • 发表日期：

    2025年8月13日

查看全文
  • Meixin Huang,
  • Jianguo Zheng

摘要

Accurate prediction of power demand patterns plays a fundamental role in optimizing electrical distribution infrastructure, enhancing operational efficiency within the energy sector, and supporting sustainable socioeconomic growth across geographical regions. Accurate forecasting provides critical guidance for policymakers and industry stakeholders. In the context of the accelerating shift towards electric energy, analyzing historical consumption trends and accurately predicting future demands is essential. This study introduces a novel PSO−optimized FGM(1,1) model to enhance forecasting accuracy. Applying this model, the study forecasts electricity consumption across 31 regions in China for the period 2025−2035. Furthermore, grey relational analysis (GRA) is utilized to assess the interdependence between electrical energy utilization and carbon dioxide emissions (CO \(_2\) ), offering valuable insights for optimizing grid planning and supporting sustainable energy policies. The findings contribute practical value to energy distribution planning, carbon management, and environmental policy development.