Monthly electricity sales forecasting of different industries based on similar month and Elman neural network model

孙旺青; 刘晓峰; 何沁蔓

doi:10.3969/j.issn.1009-1831.2022.04.009

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Monthly electricity sales forecasting of different industries based on similar month and Elman neural network model

更新时间：2026-03-05

- Monthly electricity sales forecasting of different industries based on similar month and Elman neural network model
- Vol. 24, Issue 4, (2022)
- 作者机构：
  
  南京师范大学南瑞电气与自动化学院,南京,210023
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2022.04.009
  CLC：
- Published：2022
- 稿件说明：
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孙旺青, 刘晓峰, 何沁蔓. Monthly electricity sales forecasting of different industries based on similar month and Elman neural network model[J]. 2022, 24(4).
DOI：

孙旺青, 刘晓峰, 何沁蔓. Monthly electricity sales forecasting of different industries based on similar month and Elman neural network model[J]. 2022, 24(4). DOI： 10.3969/j.issn.1009-1831.2022.04.009.

摘要

为了提高售电量预测的精度并完善售电量预测体系，提出了一种结合历史相似月的Elman神经网络组合预测模型。利用历史相似月模型可以快速辨别历史数据的特点，通过对各类售电对象的详细数据及外部影响因素分析处理，找到与待预测月目标售电对象相类似的一组历史数据，作为Elman神经网络的输入数据来完成该类售电对象的预测。然后将各售电对象的预测数据组合得到总月度预测售电量。算例仿真研究表明，该组合预测方法与单一Elman神经网络预测方法相比，预测精度更高，收敛性能更好，具有较好的应用前景。

Abstract

In order to increase the accuracy and improve the forecasting system of electricity sales

a combined forecasting model of Elman neural network combined with historical similar monthis proposed. Combined with the characteristics of rapid identification among historical data

a set of historical data similar to the forecasted month is found by analyzing and processing the detailed data and external influencing factors of electricity sales objects.This set of historical data is used as the input data of Elman neural network to complete the prediction of such sales objects. Then

the forecast data of each electricity sales object is combined to get the total monthly forecast electricity sales. The simulation result shows that compared with the single Elman neural network

the combined prediction method has higher prediction accuracy

better convergence performance and a good application prospect.

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