Application of quantile regression method in prediction of daily electricity consumption in mid summer

穆海振

doi:10.3969/j.issn.1009-1831.2018.03.006

您当前的位置：

首页 >

文章列表页 >

Application of quantile regression method in prediction of daily electricity consumption in mid summer

更新时间：2026-03-05

- Application of quantile regression method in prediction of daily electricity consumption in mid summer
- Vol. 20, Issue 3, (2018)
- 作者机构：
  
  上海市气候中心,上海,200030
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2018.03.006
  CLC：
- Published：2018
- 稿件说明：
移动端阅览
穆海振. Application of quantile regression method in prediction of daily electricity consumption in mid summer[J]. 2018, 20(3).
DOI：

穆海振. Application of quantile regression method in prediction of daily electricity consumption in mid summer[J]. 2018, 20(3). DOI： 10.3969/j.issn.1009-1831.2018.03.006.

摘要

用南方某市2010—2012年盛夏期间日用电量和日气温、降水、相对湿度等数据，探索了分位数回归方法在日用电量预测中的应用。均一化处理后得到的日用电量系数序列剔除了经济社会发展和双休日等因素影响，相关分析表明其变化与前日用电量和当日最高气温变化的关系最为密切；将前日用电量和当日最高气温作为预报因子建立分位数回归方程，独立样本检验结果表明预测效果良好；与常用的均值回归等方法相比，分位数回归方法能够给出预测值的条件概率分布情况，可为电力调度和风险管理提供更多参考信息，具有较好的应用前景。

Abstract

The 2010—2012 midsummer daily electricity con sumption and temperature

precipitation

relative humidity of one southern city were used to explore the application of quantile regres

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design and implementation of virtual power plant system supporting dynamic aggregation of equipment

Short-term power load forecasting based on load decomposition and identification

Load forecasting and adjustable potential analysis of silicon carbide enterprises based on CNN-Attention-BiLSTM

Study on the influence of environmental climate change on the load of fishery electrical equipment

Multi-featured power load forecasting based on VMD-SSA-BiLSTM

Related Author

王孟强

黄澍

孟洪民

诸葛雪迎

刘泽三

徐哲男

袁越

邓振立

Related Institution

国网信息通信产业集团有限公司

国网河南省电力公司经济技术研究院

河海大学电气与动力工程学院

国网甘肃省电力公司兰州供电公司

国网甘肃省电力公司

AI问答

⁰