Monthly electricity forecasting based on K⁃L information and ARIMA error correction

陈明帆; 宁光涛; 李琳玮; 何礼鹏; 刘丽新

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Monthly electricity forecasting based on K⁃L information and ARIMA error correction

更新时间：2026-03-05

- Monthly electricity forecasting based on K⁃L information and ARIMA error correction
- Vol. 23, Issue 2, (2021)
- 作者机构：
  
  1. 海南电网有限公司,海口,570204
  2. 北京清软创新科技股份有限公司,北京,100085
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2021
- 稿件说明：
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陈明帆, 宁光涛, 李琳玮, et al. Monthly electricity forecasting based on K⁃L information and ARIMA error correction[J]. 2021, 23(2).
DOI：

陈明帆, 宁光涛, 李琳玮, et al. Monthly electricity forecasting based on K⁃L information and ARIMA error correction[J]. 2021, 23(2). DOI：

摘要

为考虑多因素对电量的影响及提高月电量预测准确率，提出基于K-L信息量法和ARIMA误差修正的月度电量预测方法。在筛选相关性强的指标基础上，利用相关分析法对影响指标与电量进行回归建模，计算拟合误差并构建新的非平稳时间序列，结合ARIMA模型对此序列进行修正，进而获得准度性更佳的月度电量预测值，具有较高的应用价值。

Abstract

In order to consider the influence of multiple factors on electricity and improve the accuracy of monthly electricity forecasting. A monthly electricity forecasting method is proposed based on the K-L information method and ARIMA error correction. Based on screening relevant indicators

the correlation analysis method is used to make regression modeling on the influence indicators and electricity

calculate of fitting errors

and construct a new non - stationary time series

combined with the ARIMA model to modify this series. Monthly electricity predictions obtained with better accuracy

have higher application value.

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