Research on power load forecasting model of economic development zone based on neural network

被引：6
|
作者：
Feng, Qiming [1 ]
Qian, Suping [2 ]
机构：
[1] Wuxi Inst Commerce, Dept Math, Wuxi 214153, Jiangsu, Peoples R China
[2] Wuxi Inst Commerce, Sch Accounting & Finance, Wuxi 214153, Jiangsu, Peoples R China
来源：
ENERGY REPORTS | 2021年 / 7卷
关键词：
Power load forecasting; Economic development zone; Elman neural network; Mid-long term;
D O I：
10.1016/j.egyr.2021.09.098
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The power load forecasting model can calculate the predicted value accurately and quickly, which will be helpful to distribute the power reasonably and improve the stability of the power grid. There are many uncertain factors involved in medium and long-term load forecasting, and the relationship between them is seriously nonlinear and dynamic, so the prediction accuracy of traditional methods is not high. However, dynamic regression neural network can display the dynamic characteristics of the power system more accurately. According to the results of power load characteristic analysis, the article discusses the application of Elman neural network in the medium-term and long-term power load forecasting of an economic development zone in Jiangsu Province, and realizes the complex mapping from the relevant historical power load to the forecasting target. The results show that this power load forecasting method is effective and practical. (C) 2021 Published by Elsevier Ltd.
引用
收藏
页码：1447 / 1452
页数：6
相关论文
共 50 条
  • [1] Research on power load forecasting based on elman neural network
    [J]. Jiang, Zhen (jiangzhen3812@21cn.com), 2018, SHPMedia Sdn Bhd (COMPENDIUM 1):
  • [2] A power load forecasting model based on a combined neural network
    Li, Jie
    Qiu, Chenguang
    Zhao, Yulin
    Wang, Yuyang
    [J]. AIP ADVANCES, 2024, 14 (04)
  • [3] Research on Power Load Forecasting Based on the Improved Elman Neural Network
    Zhang, Jiarui
    [J]. 3RD INTERNATIONAL CONFERENCE ON APPLIED ENGINEERING, 2016, 51 : 589 - 594
  • [4] Research of artificial neural network based on data mining technology in power load forecasting model
    Niu, Dongxiao
    Wang, Yongli
    Xing, Mian
    [J]. DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2007, 14 : 388 - 392
  • [5] Research on power load forecasting model based on hybrid algorithm optimizing BP neural network
    Information Management Office, North China Electric Power University, Beijing
    102206, China
    不详
    102206, China
    不详
    102206, China
    [J]. Open Electr. Electron. Eng. J., 1 (723-728):
  • [6] Research on power load forecasting model using optimal Probabilistic neural network
    Meng Ming
    Lu Jian-chang
    Sun Wei
    [J]. PROCEEDINGS OF 2006 CHINESE CONTROL AND DECISION CONFERENCE, 2006, : 1427 - 1430
  • [7] Power Load Forecasting Based on Elman Neural Network
    Zhang Heng
    Tao Huanqi
    [J]. 2009 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL IV, 2009, : 374 - 376
  • [8] Power Load Forecasting Based on Grey Neural Network
    Zhang, Dahai
    Ren, Zhiwu
    Bi, Yanbing
    Zhou, Dazhou
    Bi, Yanqiu
    [J]. 2008 IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, VOLS 1-5, 2008, : 1214 - +
  • [9] Power Load Forecasting in the Spring Festival Based on Feedforward Neural Network Model
    Ren ZhiChao
    Ye Qiang
    Wang Haiyan
    Cheng Chao
    Liang Yuan
    [J]. PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC), 2017, : 2855 - 2858
  • [10] Power Load Forecasting Based on BP Neural Network Fractional PID Model
    Pan, Qi
    Mao, Shu-Jun
    Li, Wei
    [J]. 2015 INTERNATIONAL CONFERENCE ON ENVIRONMENT, MANUFACTURING INDUSTRY AND ECONOMIC DEVELOPMENT, (EMIED 2015), 2015, : 274 - 280
12345