中国电力 ›› 2018, Vol. 51 ›› Issue (6): 178-184.DOI: 10.11930/j.issn.1004-9649.201803151

• 技术经济 • 上一篇    

北京市电力消耗驱动因素分析及需求预测

汪斌1, 张欣欣1, 嵇灵2, 解玉磊1   

  1. 1. 北京科技大学 能源与环境工程学院, 北京 100083;
    2. 北京工业大学 经济与管理学院, 北京 100124
  • 收稿日期:2018-03-28 修回日期:2018-04-29 出版日期:2018-06-05 发布日期:2018-06-12
  • 通讯作者: 解玉磊(1985-),男,通信作者,讲师,从事能源环境系统分析研究,E-mail:xieyulei001@ustb.edu.cn
  • 作者简介:汪斌(1970-),男,博士研究生,从事能源系统优化及管理研究,E-mail:27050486@qq.com;张欣欣(1957-),男,教授,从事能源系统工程方面研究,E-mail:zhangxx011@gmail.com;嵇灵(1987-),女,博士,讲师,从事能源系统规划、低碳市场、能源互联网等方面的研究,E-mail:hdjiling@126.com
  • 基金资助:
    国家自然科学基金资助项目(71603016);中央高校基本科研业务费资助项目(FRF-TP-15-083A1)。

Driving Factor Analysis for the Power Consumption and Load Forecasting of Beijing City

WANG Bin1, ZHANG Xinxin1, JI Ling2, XIE Yulei1   

  1. 1. School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China;
    2. School of Economics and Management, Beijing University of Technology, Beijing 100124, China
  • Received:2018-03-28 Revised:2018-04-29 Online:2018-06-05 Published:2018-06-12
  • Supported by:
    This work is supported by National Natural Science Foundation of China (No.71603016) and the Fundamental Research Funds for the Central Universities (No.FRF-TP-15-083A1).

摘要: 可靠有效的中长期电力需求预测是电力生产输送的重要依据。提出基于指数分解分析的电力需求预测方法,该方法可以深入挖掘电力需求变化的关键因素及其贡献程度,并且结合社会经济发展和相关政策,通过对关键因素未来变化趋势的多情景设置,开展中长期电力需求预测。以北京市为例,对1985—;2014年北京市电力需求增长的主要因素及其贡献水平进行分解分析。结合北京市社会经济发展定位和应对气候变化的环境政策,对“十三五”期间北京市电力需求进行预测。

关键词: 中长期预测, 电力需求, 指数分解分析, 节能减排

Abstract: Reliable and effective medium- and long-term load forecasting is an important basis for power generation and transportation. To achieve this goal, an exponential decomposition analysis based load forecasting method is proposed in this paper, identifying and sorting the key effect factors. Combining the variation trends of the identified key effect factors as well as the social economy, multiple simulation scenarios can be generated for medium- and long-term load forecasting. The historical load and socio-economic data of Beijing city from 1985 to 2014 are selected as the input, and the key effect factors for the load growth are demonstrated. Finally, this paper ends with the load forecasting of Beijing city for the 13th Five-Year Period.

Key words: medium- and long-term load forecasting, electricity demand, index decomposition analysis (IDA), energy conservation and emission reduction

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