Demand forecasting analysis of electric energy meter based on seasonal exponential smoothing

周红勇; 孙雨婷; 张延展

doi:10.3969/j.issn.1009-1831.2024.02.015

您当前的位置：

首页 >

文章列表页 >

Demand forecasting analysis of electric energy meter based on seasonal exponential smoothing

更新时间：2026-03-05

- Demand forecasting analysis of electric energy meter based on seasonal exponential smoothing
- Vol. 26, Issue 2, (2024)
- 作者机构：
  
  国网江苏省电力有限公司营销服务中心,南京,210019
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2024.02.015
  CLC：
- Published：2024
- 稿件说明：
移动端阅览
周红勇, 孙雨婷, 张延展. Demand forecasting analysis of electric energy meter based on seasonal exponential smoothing[J]. 2024, 26(2).
DOI：

周红勇, 孙雨婷, 张延展. Demand forecasting analysis of electric energy meter based on seasonal exponential smoothing[J]. 2024, 26(2). DOI： 10.3969/j.issn.1009-1831.2024.02.015.

摘要

随着智能电能表大规模普及应用

电能表需求数量日益庞大

采用经验人工估计容易造成电能表临时短缺和库存积压等问题。基于计量系统中的电能表历史安装数据

采用简单季节性模型、Winters加法模型和Winters乘法模型

并结合LSTM模型对结果进行对比分析

比较不同模型的优劣

确定电能表需求预测最优方法。实证结果表明：Winters乘法模型预测效果最好

预测值平均误差为2.46%

预测趋势符合实际情况。Winters乘法指数平滑法能够对电能表需求变化趋势进行科学合理预测

可以协助供电公司更加高效开展电能表运营管理

提高计量资产利用效率。

Abstract

With the widespread application of intelligent energy meters

the demand for energy meters is becoming increasingly large. Using empirical manual estimation can easily lead to temporary shortages and inventory backlog of energy meters. Based on the historical installation data of electricity meters in the metering system

simple seasonal model

Winters addition model

and Winters multiplication model are used. And LSTM model is combined to compare and analyze the results

compare the advantages and disadvantages of different models

and determine the optimal method for predicting electricity meter demand. The empirical results indicate that the Winters multiplication model has the best prediction effect

with an average error of 0.96% in the predicted values

and the predicted trend is in line with the actual situation. Winters multiplication index smoothing method can scientifically and reasonably predict the trend of electricity meter demand changes

assist power supply companies in more efficient operation and management of electricity meters

and improve the efficiency of measuring asset utilization.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Life prediction of electric energy meter based on shared data sources and combined life stress model

Techniques to Protect Wood from Blue staining and to Remove Blue stains from Fungi Attacked Timber

Studies on Bonding Mechanism of Dry process Binderless Fiberboard Part Ⅱ.FTIR Study of Binderless Fiberboard

Optimization for Overlaying Technology of OSB with Resin impregnated Paper

热风干燥槠、栲类木材工艺探索及其应用

Related Author

吴建平

廖绍成

曹君波

Related Institution

国网浙江新昌县供电公司

AI问答

⁰