Research and Application of Short-term Power Load Based on Large Data Analysis

被引:0
|
作者
Ma, Zhi-cheng [1 ]
Yang, Peng [1 ]
Zhang, Lei [1 ]
Zhao, Qiang [2 ]
Zhang, Wen-qiang [2 ]
机构
[1] State Grid Power Co, Informat & Commun Co, Lanzhou, Gansu, Peoples R China
[2] North China Elect Power Univ, Beijing, Peoples R China
关键词
Hadoop; SVM; Spark;
D O I
暂无
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
We want to carry out an effectively distributed method to process large power data. Large data of electricity is first classified by big industries, and then by small industries, and finally put to use clustering to distinguish electro-sort of customers. This paper employed a computational framework named Spark based memory computing to conduct distributed computing, and adopted supporting vector machine on the Spark's, lastly the results were analyzed. Ensuring the final effect of the test, solution speed is greatly improved.
引用
收藏
页码:69 / 72
页数:4
相关论文
共 50 条
  • [31] Short-term power load forecasting using integrated methods based on long short-term memory
    ZHANG WenJie
    QIN Jian
    MEI Feng
    FU JunJie
    DAI Bo
    YU WenWu
    Science China(Technological Sciences), 2020, 63 (04) : 614 - 624
  • [32] Short-term power load forecasting using integrated methods based on long short-term memory
    Zhang, WenJie
    Qin, Jian
    Mei, Feng
    Fu, JunJie
    Dai, Bo
    Yu, WenWu
    SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2020, 63 (04) : 614 - 624
  • [33] Short-term power load forecasting using integrated methods based on long short-term memory
    ZHANG WenJie
    QIN Jian
    MEI Feng
    FU JunJie
    DAI Bo
    YU WenWu
    Science China(Technological Sciences), 2020, (04) : 614 - 624
  • [34] Short-term power load forecasting using integrated methods based on long short-term memory
    WenJie Zhang
    Jian Qin
    Feng Mei
    JunJie Fu
    Bo Dai
    WenWu Yu
    Science China Technological Sciences, 2020, 63 : 614 - 624
  • [35] A data mining method for short-term load forecasting in power systems
    Mori, H
    Kosemura, N
    ELECTRICAL ENGINEERING IN JAPAN, 2002, 139 (02) : 12 - 22
  • [36] The power load’s signal analysis and short-term prediction based on wavelet decomposition
    Huan Wang
    Min Ouyang
    Zhibing Wang
    Ruishi Liang
    Xin Zhou
    Cluster Computing, 2019, 22 : 11129 - 11141
  • [37] The power load's signal analysis and short-term prediction based on wavelet decomposition
    Wang, Huan
    Ouyang, Min
    Wang, Zhibing
    Liang, Ruishi
    Zhou, Xin
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 5): : 11129 - 11141
  • [38] Short-Term Load Forecasting Based on Big Data Technologies
    Zhang, Pei
    Wu, Xiaoyu
    Wang, Xiaojun
    Bi, Sheng
    CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 2015, 1 (03): : 59 - 67
  • [39] Research on Short-Term Power Load Forecasting Model Based on NGO-CNN-BIGRU-AT
    Yuan, Qingyun
    Yu, Pan
    Tan, Liu
    Wang, Yonggang
    Zhang, Heming
    IAENG International Journal of Computer Science, 2024, 51 (08) : 1163 - 1170
  • [40] Application of artificial neural networks to historical data analysis for short-term electric load forecasting
    Caciotta, M
    Lamedica, R
    Cencelli, VO
    Prudenzi, A
    Sforna, M
    EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, 1997, 7 (01): : 49 - 56