Power short-term load forecasting based on big data and optimization neural network

Xin JIN; Long-wei LI; Jia-nan JI; Zhi-qi LI; Yu HU; Yong-bin ZHAO

doi:10.11959/j.issn.1000-436x.2016245

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Power short-term load forecasting based on big data and optimization neural network

Contents Papers | 更新时间：2024-06-05

- Power short-term load forecasting based on big data and optimization neural network
- Journal on Communications Vol. 37, Issue Z1, Pages: 36-42(2016)
- 作者机构：
  
  1. 中央财经大学信息学院，北京 100081
  2. 人力资源和社会保障部人事考试中心，北京100011
  3. 北京国电通网络技术有限公司，北京100070
  4. 国网辽宁省电力有限公司信息通信分公司，辽宁沈阳110006
- 作者简介：
- 基金信息：
  
  TheNationalNaturalScienceFoundationofChina(U1509214);Technology Project of State Grid Corpora-tion of China(SGTYHT/14-JS-188)
- DOI：10.11959/j.issn.1000-436x.2016245
  CLC： TP391
- Online First：2016-10，
  
  Published：25 October 2016
- 稿件说明：
移动端阅览
Xin JIN, Long-wei LI, Jia-nan JI, et al. Power short-term load forecasting based on big data and optimization neural network[J]. Journal on Communications, 2016, 37(Z1): 36-42.
DOI：

Xin JIN, Long-wei LI, Jia-nan JI, et al. Power short-term load forecasting based on big data and optimization neural network[J]. Journal on Communications, 2016, 37(Z1): 36-42. DOI： 10.11959/j.issn.1000-436x.2016245.

摘要

随着电力数据采集成本降低及大规模电网互联等因素，电网中可获取的数据类型日益丰富。以往的集中式预测方法对海量电力数据的分析能力有限。提出基于大数据和粒子群优化BP神经网络短期电力负荷预测，建立短期电力负荷预测模型。利用国家电网的实际负荷数据，采用所提方法进行预测，与实际负荷数据及集中式负荷预测结果进行比较，结果证明，所提方法预测精度较高，降低了负荷预测时间，在实际应用中具有可行性。

Abstract

With the reduction of the cost of power data acquisition and the interconnection of large scale power systems

the types of data available in the power network are becoming more and more abundant.In the past

the centralized fore-casting method was limited to the analysis of the massive power data.Therefore

a short-term power load forecasting based on large data and particle swarm optimization BP neural network was proposed

and short-term power load fore-casting model was established.The actual load data of the national grid

using the method of prediction

compared with the actual load data and centralized load forecasting results prove that this method is accurate enough

reduce the load forecasting time with feasibility in practical application.

关键词

Keywords

references

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