计算机工程

• 先进计算与数据处理 • 上一篇    下一篇

基于Storm的电网时间序列数据实时预测框架

吴克河,朱亚运,李皓阳,李权   

  1. (华北电力大学 控制与计算机工程学院,北京 102206)
  • 收稿日期:2016-06-08 出版日期:2017-04-15 发布日期:2017-04-14
  • 作者简介:吴克河(1962—),男,教授、博士,主研方向为电力大数据、电力信息安全;朱亚运(通信作者),博士研究生;李皓阳、李权,硕士研究生。
  • 基金项目:
    中央高校基本科研业务费专项资金(2015XS72)。

Real-time Predication Framework for Power Grid Time-series Data Based on Storm

WU Kehe,ZHU Yayun,LI Haoyang,LI Quan   

  1. (School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)
  • Received:2016-06-08 Online:2017-04-15 Published:2017-04-14

摘要: 对电网运行产生的时间序列数据展开实时预测研究,提出基于Storm平台和ARIMA模型的预测框架。分析不同类型电网时序数据的特点,预设拟合模型以降低模型构建的盲目性,缩短预测时间,同时设计基于HBase的新型时序数据存储模式加快数据检索速度。通过对海量的时序数据源进行并发预测,比较不同数据样本对预测值的影响并实时分析预测误差。经实例从预测精度、运算速度、占用资源3个角度验证了该框架的有效性与实用性。

关键词: 时间序列数据, 实时预测, Storm平台, 自回归积分移动平均模型, 电网, 大数据

Abstract: This paper researches on the real-time predication of power grid Time-series Data(TSD) and puts forward a predication framework based on Storm platform and Autoregressive Integrated Moving Average(ARIMA) model.It analyzes the characteristics of different types of power grid TSD and presets fitting model to reduce the blindness of model building and to shorten the time of predication.Meanwhile,it designs a new storage mode for TSD based on HBase to accelerate the speed of data retrieval.It compares the influences of different data samples on the results of predication and analyzes the prediction error in real time through the concurrent prediction of massive TSD sources.Finally,three aspects including prediction precision,computing speed and resource occupancy are chosen to verify the effectiveness and practicability of the proposed framework by authentic cases.

Key words: Time-series Data(TSD), real-time predication, Storm platform, Autoregressive Integrated Moving Average(ARIMA) model, power grid, big data

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