Prediction of Short-Term Load Based on Big Data Mining

Chen Li; Cao Xi; Lin Jun-jie; Gao Hong-ming; Liu Fei-ya; Li Yan-yan

doi:10.12052/gdutxb.170044

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Prediction of Short-Term Load Based on Big Data Mining

更新时间：2026-05-27

- Prediction of Short-Term Load Based on Big Data Mining
- Vol. 34, Issue 3, Pages: 105-109,114(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170044
  CLC： TM714
- Online First：09 May 2017，
  
  Published：2017
- 稿件说明：
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陈丽, 曹熙, 林俊杰, 高鸿铭, 刘飞雅, 李艳艳. 基于数据挖掘的短期电力负荷风险预测分析[J]. 广东工业大学学报, 2017, 34(3): 105-109,114.

Chen Li, Cao Xi, Lin Jun-jie, et al. Prediction of Short-Term Load Based on Big Data Mining[J]. 2017, 34(3): 105-109,114.
陈丽, 曹熙, 林俊杰, 高鸿铭, 刘飞雅, 李艳艳. 基于数据挖掘的短期电力负荷风险预测分析[J]. 广东工业大学学报, 2017, 34(3): 105-109,114. DOI： 10.12052/gdutxb.170044.

Chen Li, Cao Xi, Lin Jun-jie, et al. Prediction of Short-Term Load Based on Big Data Mining[J]. 2017, 34(3): 105-109,114. DOI： 10.12052/gdutxb.170044.

摘要

电力负荷风险越来越成为电力生产行业关注的热点，传统的电力负荷风险预测仅仅从单因素测评台区的风险度，缺乏全面和系统性.因此，传统的预测方法，不能准确地预测风险因素造成的电力故障隐患.为解决此问题，从供电局客服数据、机器监测台区记录、天气等多数据源着手，对电力负荷风险进行分析和预测.首先，对数据进行清洗和分类.然后，利用K-Mean聚类筛选出与电力负荷相关性强的因素作为模型的变量.并在此基础上，构建基于贝叶斯判别的台区电力风险预测模型.通过数据实验，该模型能够以99.53%的准确度来预估台区的负荷风险，从而进行有效的电力故障预测判断，为电力企业传送电的风险防范和控制决策提供支持，降低客户的用电故障，提高客户满意度.

Abstract

The risk of power load becomes the hot spot in the electric power industry; however

due to the single factor evaluation

the traditional power load forecasting model is not adequately comprehensive and systematic. Hence

it cannot accurately predict the risk and may cause hidden danger of power failures. To address this issue

the risk of power load is analyzed and forecast by collecting data from multiple sources:customer service center

machine

and historical weather records and so on. First by cleaning and sorting the data and then by the K-Mean clustering

variables are chosen which have strong correlation with risk degree of transformer to construct the Bayesian discriminant models. The experimental results show that this model can accurately predict the risk of transformer at a certain probability of 99.53%. In the practical aspect

this model can provide prevention scheme and control decisions to power supply security and contribute to reduce customer's electricity failure and improve customer satisfaction.

关键词

Keywords

references

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