基于用电模式数的大用户负荷短期预测技术研究
作者:
基金项目:

国家重点研发计划资助项目(2016YFB0901102);国家重点研发计划资助项目(2016YFB0901104)


Study on Short-term Forecasting of Large Users Load basedon Power Consumption Model Numbers
Author:
Fund Project:

National Key Research and Development Program of China (2016YFB0901102)

  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [17]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    针对传统负荷预测方法适用性差问题,文中通过月均负荷率和负荷标准差与月均负荷率的比值对大用户进行划分,明确大用户的负荷特性分类。在此基础上,对分类用户的典型负荷曲线、波动曲线、连续负荷曲线进行分析,得出各类用户的用电模式数,再分别根据模式数制定针对性预测方法,提高可调度容量预测的精确度,为电力营销需求响应业务的快速发展提供技术支撑。

    Abstract:

    In view of the poor applicability of the traditional load forecasting methods,this paper divides the large users by two parameters,namely monthly load rate and the ratio of load standard deviation to monthly load rate,in order to clear the classification of load characteristics of large consumers.On this basis,the typical load curve,fluctuation curve and continuous load curve of the classified users are analyzed to obtain the power mode numbers.Then,the paper formulates the targeted prediction methods according to model numbers to improve the accuracy of the prediction of schedulability capacity and to provide technical support for the rapid development of power market demand response business.

    参考文献
    [1] 王珂,姚建国,姚良忠,等.电力柔性负荷调度研究综述[J].电力系统自动化,2014,38(20):127-135. WANG Ke,YAO Jianguo,YAO Liangzhong,et al. Survey of research on flexible loads scheduling technologies[J]. Automation of Electric Power Systems,2014,38(20):127-135.
    [2] 牛东晓,陈志强.电力市场下的需求响应研究[J].华东电力,2008(9):5-9. NIU Dongxiao,CHEN Zhiqiang. Demand response in electricity markets[J]. East China Electric Power,2008(9):5-9.
    [3] 马燕军,陈嘉曦,华亮亮,等.电力需求侧的超短期负荷预测分析[J].机电信息,2015(21):122-123,125. MA Yanjun,CHEN Jiaxi,HUA Liangliang,et al. Study on short-term forecasting of power demand-side[J].Mechanical and Electrical Information,2015(21):122-123,125.
    [4] 马朝,王莹,史雷.基于组合模型的制造业用电量预测[J].管理观察,2017(9):34-36. MA Chao,WANG Ying,SHI lei. Prediction of electricity consumption in manufacturing industry based on combination model[J]. Management Observer,2017(9):34-36.
    [5] 包哲静.支持向量机在智能建模和模型预测控制中的应用[D].杭州:浙江大学,2007. BAO Zhejing. Applications of support vector machine in intelligent modeling and model predictive control[D]. Hangzhou:Zhejiang University,2007.
    [6] 张素香,赵丙镇,王风雨,等.海量数据下的电力负荷短期预测[J].中国电机工程学报,2015,35(1):37-42. ZHANG Suxiang,ZHAO Bingzhen,WANG Fengyu,et al.Short-term power load forecasting based on big data[J]. Proceedings of the CSEE,2015,35(1):37-42.
    [7] 张钦,王锡凡,王建学,等.电力市场下需求响应研究综述[J].电力系统自动化,2008,32(3):97-106. ZHANG Qin,WANG Xifan,WANG Jianxue,et al. Survey of demand response research in deregulated electricity markets[J]. Automation of Electric Power Systems,2008,32(3):97-106.
    [8] 陈明照,毛坚,杜宗林,等.基于聚类法的工业用户需求侧管理(DSM)方案分析与研究[J].电力系统保护与控制,2017,45(7):84-89. CHEN Mingzhao,MAO Jian,DU Zonglin,et al. Analysis on demand side management scheme of industrial enterprise based on clustering method[J]. Power System Protection and Control,2017,45(7):84-89.
    [9] 孙海斌,李扬,卢毅,等.电力系统短期负荷预测方法综述[J].江苏电机工程,2000(2):9-13,17. Sun Haibin,Li Yang,Lu Yi,et al. Synthesis of the short-term load forecast method of power system[J]. Jiangsu Electrical Engineering,2000(2):9-13,17.
    [10] 王德文,孙志伟.电力用户侧大数据分析与并行负荷预测[J].中国电机工程学报,2015,35(3):527-537. WANG Dewen,SUN Zhiwei. Big data analysis and parallel load forecasting of electric power user side[J]. Proceedings of the CSEE,2015,35(3):527-537.
    [11] 曾鸣,李娜,王涛,等.兼容需求侧资源的负荷预测新方法[J].电力自动化设备,2013,33(10):59-62,73. ZENG Ming,LI Na,WANG Tao,et al. Load forecasting compatible with demand-side resources[J]. Electric Power Automation Equipment,2013,33(10):59-62,73.
    [12] 程建东,杜积贵.组合预测方法在电力负荷预测中的应用[J].江苏电机工程, 2011, 30(6):38-40,44. Cheng Jiandong,Du Jigui. Application of combined method in power load forecasting[J]. Jiangsu Electrical Engineering, 2011, 30(6):38-40,44.
    [13] 沈沉,秦建,盛万兴,等.基于小波聚类的配变短期负荷预测方法研究[J].电网技术,2016,40(2):521-526. SHEN Chen,QIN Jian,SHENG Wanxing,et al. Study on short-term forecasting of distribution transformer load using wavelet and clustering method[J]. Power System Technology,2016,40(2):521-526.
    [14] 林启开,王珂,余坤,等.峰谷电价下居民用电聚合响应特性分析[J].电力工程技术,2017,36(3):88-93. Lin Qikai,Wang Ke,Yu Kun,et al. Analysis on the polymeric response characteristics of residents under the peak and valley electricity price[J]. Electric Power Engineering Technology,2017,36(3):88-93.
    [15] 宗柳,李扬,王蓓蓓.计及需求响应的多维度用电特征精细挖掘[J].电力系统自动化,2012,36(20):54-58. ZONG Liu,LI Yang,WANG Beibei. Fine-mining of multi-dimension electrical characteristics consi-dering demand response[J]. Automation of electric power system,2012,36(20):54-58.
    [16] 陶小马,周雯.电力需求响应的研究进展及文献述评[J].北京理工大学学报(社会科学版),2014,16(1):32-40. TAO Xiaoma,ZHOU Wen. A review of the research on electricity demand response[J]. Journal of Beijing Institute of Technology (Social Sciences Edition),2014,16(1):32-40.
    [17] 马小慧,阳育德,龚利武.基于Kohonen聚类和SVM组合算法的电网日最大负荷预测[J].电网与清洁能源,2014,30(2):7-11. MA Xiaohui,YANG Yude,GONG Liwu. Forecasting of the daily maximum load based on a combined model of Kohonen clustering and SVM[J]. Power System and Clean Energy,2014,30(2):7-11.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

栾开宁,鲍敏,易永仙,赵双双.基于用电模式数的大用户负荷短期预测技术研究[J].电力工程技术,2018,37(3):33-37

复制
分享
文章指标
  • 点击次数:1679
  • 下载次数: 2544
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 收稿日期:2018-01-13
  • 最后修改日期:2018-02-24
  • 录用日期:2017-12-15
  • 在线发布日期: 2018-05-29
  • 出版日期: 2018-05-28
文章二维码