Application of Hybrid Model Based on Machine Learning in Power Load Forecasting

XU Jianping; HU Jiheng; SUN Weizhe

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Application of Hybrid Model Based on Machine Learning in Power Load Forecasting

更新时间：2025-04-27

- Application of Hybrid Model Based on Machine Learning in Power Load Forecasting
- Vol. 40, Issue 1, Pages: 27-30(2021)
- 作者机构：
  
  1. 国网浙江省电力有限公司金华供电公司
  2. 2. 北京众智新能科技有限公司
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM715;TP181
- Published：2021
- 稿件说明：
移动端阅览
[1]徐建平,胡济恒,孙卫哲.基于机器学习的混合模型在电力负荷预测中的应用[J].河北电力技术,2021,40(01):27-30.

XU Jianping, HU Jiheng, SUN Weizhe. Application of Hybrid Model Based on Machine Learning in Power Load Forecasting[J]. 2021, 40(1): 27-30.
[1]徐建平,胡济恒,孙卫哲.基于机器学习的混合模型在电力负荷预测中的应用[J].河北电力技术,2021,40(01):27-30. DOI：

XU Jianping, HU Jiheng, SUN Weizhe. Application of Hybrid Model Based on Machine Learning in Power Load Forecasting[J]. 2021, 40(1): 27-30. DOI：

摘要

从电力负荷的变化特点入手

提出基于机器学习的混合模型

该模型采用具有自适应噪声的完整集合经验模式分解技术对电力负荷进行分解

剔除高频噪声影响后

对负荷序列进行重构

采用多目标花授粉优化算法对极限学习机进行优化

分析认为该模型可提高极限学习机预测的准确性和稳定性

结合江苏省月度负荷预测案例验证了模型的有效性。

Abstract

Starting from the changing characteristics of the power load

a hybrid model based on machine learning is proposed

and the model is analyzed using a complete set of empirical mode decomposition technology with adaptive noise to decompose the power load.After removing the influence of high-frequency noise

the load sequence is reconstructed

The process of optimizing the extreme learning machine with a multi-objective flower pollination optimization algorithm

and that the model can improve the accuracy and stability of the extreme learning machine prediction

combined with the monthly load forecasting case analysis in Jiangsu Province to verify the effectiveness of the model.

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