中国电力 ›› 2021, Vol. 54 ›› Issue (7): 1-10,26.DOI: 10.11930/j.issn.1004-9649.202007277

• 能源安全新战略路径设计与规划推演关键技术专栏 • 上一篇    下一篇

清洁能源示范省建设背景下青海能源需求预测及清洁化发展对策

李红霞, 张祥成, 李芳, 张海宁, 李楠, 马雪   

  1. 国网青海省电力公司清洁能源发展研究院,青海 西宁 810008
  • 收稿日期:2020-08-26 修回日期:2021-01-30 出版日期:2021-07-05 发布日期:2021-07-12
  • 作者简介:谭显东(1979-),男,通信作者,博士,高级工程师(教授级),从事电力供需分析预测、需求侧管理、能源经济研究,E-mail:tanxiandong@sgeri.com.cn;刘俊(1983-),男,博士,高级工程师(教授级),从事能源战略、电力规划、新能源消纳,E-mail:liujun@sgeri.com.cn
  • 基金资助:
    国家自然科学基金资助项目(我国减少清洁能源发电弃能的机制设计及其模拟模型研究,71573084)

Qinghai Energy Demand Forecasting and Development Strategy Research under the Background of Construction of Clean Energy Demonstration Province

LI Hongxia, ZHANG Xiangcheng, LI Fang, ZHANG Haining, LI Nan, MA Xue   

  1. Green Energy Development Research Institute, State Grid Qinghai Electric Power Company, Xining 810008, China
  • Received:2020-08-26 Revised:2021-01-30 Online:2021-07-05 Published:2021-07-12
  • Supported by:
    This work is supported by National Natural Science Foundation of China (Research on Mechanism Design and Simulation Models for Reducing Energy Waste of Clean Energy in China, No.71573084)

摘要: 2018年青海省获批建设国家清洁能源示范省并于同年提出《青海省建设国家清洁能源示范省工作方案(2018—2020年)》,以绿色、高效和安全为总目标,推进能源生产和消费革命,为中国能源清洁转型与现代能源体系建设贡献力量。在清洁能源示范省建设背景下,开展青海能源需求预测及清洁化发展对策研究。首先,分析青海省清洁能源发展现状以及面临的挑战;然后,通过双变异差分进化算法优化BP神经网络预测模型,建立DMDE-BPNN混合预测模型;分析青海省典型水平年能源需求预测结果,探讨青海省能源清洁化发展对策。

关键词: 能源需求预测, 清洁化发展对策, DMDE-BNPP混合预测模型, 国家清洁能源示范省

Abstract: In 2018, Qinghai Province was approved to build the National Clean Energy Demonstration Province and proposed the "Qinghai Province National Clean Energy Demonstration Province Work Plan (2018-2020)" in the same year. The work plan takes the greennesss, high efficiency and safety as the overall goal, and promotes the revolution in energy production and consumption, so as to make a contribution to the clean transition of China’s energy and the construction of a modern energy system. Under this background, a study is made on the Qinghai energy demand forecasting and clean development strategy under the background of clean energy demonstration province construction. Firstly, the paper analyzes the current status and challenges of clean energy development in Qinghai Province. Then, a DMDE-BPNN hybrid prediction model is established by optimizing the BP neural network prediction model with the double mutation differential evolution algorithm. By taking Qinghai Province as an example, the annual energy demand in typical years is forecasted, and some measures are proposed for the development of clean energy in Qinghai Province.

Key words: energy demand forecast, clean development strategy, DMDE-BPNN hybrid prediction model, national clean energy demonstration province