Two-stage Optimal Training Method of Extreme Learning Machine Forecasting Based on Natural Evolution Strategy for Renewable Energy

QUAN Hao; LYU Lizhen; GUO Jian; GE Yiwen; LIU Wei

doi:10.7500/AEPS20210901010

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Two-stage Optimal Training Method of Extreme Learning Machine Forecasting Based on Natural Evolution Strategy for Renewable Energy

Basic Research | 更新时间：2025-06-16

- Two-stage Optimal Training Method of Extreme Learning Machine Forecasting Based on Natural Evolution Strategy for Renewable Energy
- Automation of Electric Power Systems Vol. 46, Issue 20, Pages: 83-91(2022)
- 作者机构：
  
  南京理工大学自动化学院，江苏省南京市 210094
- 作者简介：
- 基金信息：
  
  This work is supported byFoundation: National Natural Science Foundation of China(51907090);Jiangsu Provincial Natural Science Foundation of China(BK20211195)
- DOI：10.7500/AEPS20210901010
  CLC：
- Received：01 September 2021，
  
  Revised：2022-03-23，
  
  Published Online：17 August 2022，
  
  Published：25 October 2022
- 稿件说明：
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权浩,吕立臻,郭健等.基于自然进化策略的可再生能源ELM预测两阶段优化训练方法[J].电力系统自动化,2022,46(20):83-91.

QUAN Hao,LYU Lizhen,GUO Jian,et al.Two-stage Optimal Training Method of Extreme Learning Machine Forecasting Based on Natural Evolution Strategy for Renewable Energy[J].Automation of Electric Power Systems,2022,46(20):83-91.
权浩,吕立臻,郭健等.基于自然进化策略的可再生能源ELM预测两阶段优化训练方法[J].电力系统自动化,2022,46(20):83-91. DOI： 10.7500/AEPS20210901010.

QUAN Hao,LYU Lizhen,GUO Jian,et al.Two-stage Optimal Training Method of Extreme Learning Machine Forecasting Based on Natural Evolution Strategy for Renewable Energy[J].Automation of Electric Power Systems,2022,46(20):83-91. DOI： 10.7500/AEPS20210901010.

摘要

随着中国“双碳”目标的提出和可再生能源接入比例的不断提高，其精确预测对提高新能源消纳水平和电网稳定性以及降低系统规划运行成本具有重要意义。为此，提出了基于自然进化策略（NES）的极限学习机（ELM）网络参数两阶段优化训练方法及可再生能源功率预测改进模型。在第1阶段，使用ELM随机参数和广义矩阵逆运算过程训练模型网络参数。ELM训练过程具有随机性且矩阵逆运算过程缺少对参数的微调，导致模型虽然训练速度较快，但在精度上有所欠缺。因此，第2阶段在原有模型训练结果的基础上引入输出层偏移和自适应的NES算法，对网络参数进行二次优化调整。结果表明

该两阶段优化算法能够有效地提高模型预测精度。

Abstract

With the proposal “carbon emission peak and carbon neutrallity” target of China and the continuous increase of the proportion of renewable energy access

accurate forecasting is of great significance to improve the level of renewable energy accommodation and grid stability

and reduce the system planning and operation cost. Therefore

a two-stage optimal training method of extreme learning machine (ELM) network parameters based on natural evolution strategy (NES) and an improved model of renewable energy power forecasting are proposed. In the first stage

random parameters and the generalized matrix inverse operation process of ELM are used to train the model network parameters. Due to the randomness of the ELM training process and the lack of fine-tuning for parameters in the matrix inverse operation process

the accuracy is deficient although the training speed of the model is fast. Therefore

in the second stage

based on the training results of the original model

the output layer bias and adaptive NES algorithm are introduced to optimize and adjust the network parameters for the second time. The results show that the proposed two-stage optimization algorithm can effectively improve the accuracy of the forecasting model

关键词

Keywords

references

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