Medium- and Long-term Forecast of China’s Electricity Consumption Considering the Fast Growth of New Infrastructure

Junchao Yang, Tianfeng Li
{"title":"Medium- and Long-term Forecast of China’s Electricity Consumption Considering the Fast Growth of New Infrastructure","authors":"Junchao Yang, Tianfeng Li","doi":"10.13052/spee1048-5236.4246","DOIUrl":null,"url":null,"abstract":"In order to predict China’s electricity consumption demand under situation of fast growth of the new infrastructure, a medium- and long-term electricity consumption demand prediction model based on LEAP is proposed, which calculates the overall electricity consumption of the country according to the activity level and intensity of typical new infrastructure and other sectors. Five different scenarios are set for comparison. The results show that under the basic scenario, China’s electricity consumption will reach 7408 billion kWh in 2035, in which the consumption of new infrastructure accounts for 31.31%.While under the other four scenarios, China’s electricity consumption will reach to the minimum of 7319 billion kWh under the scenario of improvement of energy-saving technology, and the maximum of 7525 billion kWh under the scenario of fast development of the new infrastructure, with higher contributions of the consumption of commercial charging, data centers and 5G base station. Relevant suggestions are put forward from the aspects of new infrastructure construction, development of clean energy and technical standards to reduce the electricity consumption of new infrastructure.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Strategic Planning for Energy and the Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13052/spee1048-5236.4246","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0

Abstract

In order to predict China’s electricity consumption demand under situation of fast growth of the new infrastructure, a medium- and long-term electricity consumption demand prediction model based on LEAP is proposed, which calculates the overall electricity consumption of the country according to the activity level and intensity of typical new infrastructure and other sectors. Five different scenarios are set for comparison. The results show that under the basic scenario, China’s electricity consumption will reach 7408 billion kWh in 2035, in which the consumption of new infrastructure accounts for 31.31%.While under the other four scenarios, China’s electricity consumption will reach to the minimum of 7319 billion kWh under the scenario of improvement of energy-saving technology, and the maximum of 7525 billion kWh under the scenario of fast development of the new infrastructure, with higher contributions of the consumption of commercial charging, data centers and 5G base station. Relevant suggestions are put forward from the aspects of new infrastructure construction, development of clean energy and technical standards to reduce the electricity consumption of new infrastructure.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
考虑新基础设施快速增长的中国电力消费中长期预测
为了预测新基础设施快速增长的情况下中国的用电需求,提出了一个基于LEAP的中长期用电需求预测模型,该模型根据典型新基础设施和其他部门的活动水平和强度来计算全国的总体用电。设置了五种不同的场景进行比较。结果表明,在基本情景下,2035年中国用电量将达到74080亿千瓦时,其中新型基础设施的用电量占31.31%。而在其他四种情景下,在节能技术改进的情景下,中国用电量最低将达到73190亿千瓦时,在新基础设施快速发展的情况下,最高可达75250亿千瓦时,商业充电、数据中心和5G基站的消耗贡献更大。从新型基础设施建设、清洁能源发展和技术标准等方面提出了降低新型基础设施用电量的相关建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Strategic Planning for Energy and the Environment
Strategic Planning for Energy and the Environment Environmental Science-Environmental Science (all)
CiteScore
1.50
自引率
0.00%
发文量
25
期刊最新文献
Application of Digital Economy Machine Learning Algorithm for Predicting Carbon Trading Prices Under Carbon Reduction Trends Low-Carbon Economic Dispatch of Integrated Energy Systems in Multi-Form Energy-intensive Parks Based on the ICT-GRU Prediction Model Installation Technique and Numerical Simulation of Stress on High-Pile Footings During the Translation of Offshore Booster Stations Analysis of the Factors Affecting the Logistics Efficiency of Urban Farm Products in the Context of Low-carbon Economy The Relationship Between Green Finance, Sustainable Technological Innovation and Energy Efficiency
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1