Study on characteristics and demand forecast of low⁃voltage electricity load in an urban village in Guangzhou

赖水生; 肖军; 蓝贤钢; 左小德

doi:10.3969/j.issn.1009-1831.2020.05.014

您当前的位置：

首页 >

文章列表页 >

Study on characteristics and demand forecast of low⁃voltage electricity load in an urban village in Guangzhou

更新时间：2026-03-05

- Study on characteristics and demand forecast of low⁃voltage electricity load in an urban village in Guangzhou
- Vol. 22, Issue 5, (2020)
- 作者机构：
  
  1. 广州供电局有限公司番禺供电局,广州,510000
  2. 暨南大学管理学院,广州,510000
  3. 珠海城市职业技术学院经济学院,广东,珠海,519000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2020.05.014
  CLC：
- Published：2020
- 稿件说明：
移动端阅览
赖水生, 肖军, 蓝贤钢, et al. Study on characteristics and demand forecast of low⁃voltage electricity load in an urban village in Guangzhou[J]. 2020, 22(5).
DOI：

赖水生, 肖军, 蓝贤钢, et al. Study on characteristics and demand forecast of low⁃voltage electricity load in an urban village in Guangzhou[J]. 2020, 22(5). DOI： 10.3969/j.issn.1009-1831.2020.05.014.

摘要

为解决城中村用电供需不平衡的问题，通过对广州某城中村（N村）2016—2017年的历史负荷数据分析，按照日、月、季（年）负荷特性的相关指标分类，确定了N村的负荷的变化规律及其特性。对城中村的负荷影响因素进行了分类研究，并运用“统计产品与服务解决方案”（statistical product and service solutions，SPSS）对因素进行因子分析、主成分分析和线性回归等，得到影响负荷变化的主要影响因子以及负荷预测模型，并用内插外推法验证了模型的通用性，负荷的预测可以为供电部门提供决策依据。

Abstract

In order to solve the problem of unbalanced electricity supply and demand of urban villages

the historical load data of an urban village in Guangzhou（N village）from 2016 to 2017 are analyzed. According to the relevant indicators of daily

monthly and quarterly（yearly）load characteristics

the change law and characteristics of the load of N village are determined. The load influencing factors of the urban village are classified

and SPSS is used to analyze the factors

conduct principal component analysis and complete linear regression

and the main influencing factors and load forecasting models that affect the load changes are obtained. The interpolation extrapolation method is used to verify the versatility of the model. The prediction of the load can provide the decision-making basis for the power supply department.

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