上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 6: 819 - 825

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.483

新型电力系统与综合能源

基于树结构Parzen估计器优化集成学习的短期负荷预测方法

  • 罗敏 ,
  • 杨劲锋 ,
  • 俞蕙 ,
  • 赖雨辰 ,
  • 郭杨运 ,
  • 周尚礼 ,
  • 向睿 ,
  • 童星 ,
  • 陈潇
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  • 1.南方电网数字电网研究院有限公司,广州 510000
    2.中国南方电网有限责任公司, 广州 510000
    3.深圳市橙智科技有限公司,广东 深圳 518000
罗敏(1985-),高级工程师,从事电动汽车与储能、用电大数据等研究.
童星,博士,电话(Tel.):0755-84860840;E-mail: tongxing@orait.cn.

收稿日期: 2022-11-28

  修回日期: 2023-02-17

  录用日期: 2023-03-09

  网络出版日期: 2023-05-30

基金资助

南方电网数字电网研究研究院(670000KK52210036)

收起

TPE-Based Boosting Short-Term Load Forecasting Method

  • LUO Min ,
  • YANG Jinfeng ,
  • YU Hui ,
  • LAI Yuchen ,
  • GUO Yangyun ,
  • ZHOU Shangli ,
  • XIANG Rui ,
  • TONG Xing ,
  • CHEN Xiao
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  • 1. Digital Grid Research Institute,China Southern Power Grid, Guangzhou 510000, China
    2. China Southern Power Grid Co., Ltd., Guangzhou 510000, China
    3. Shenzhen Orange Intelligence Technology Co., Ltd., Shenzhen 518000, Guangdong, China

Received date: 2022-11-28

  Revised date: 2023-02-17

  Accepted date: 2023-03-09

  Online published: 2023-05-30

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摘要

短期负荷预测主要用于电力系统实时调度、日前发电计划的制定,对电力系统经济调度、系统的安全运行具有重要意义.国内外在采用智能模型进行短期负荷预测方面开展了大量研究,然而智能预测方法的预测效果较易受到现存方法结构及参数的影响,以及预测对象自身个性差异使得参数难以复用,如何精准快速地获取方法结构与参数成为短期负荷预测的关键难题.对此,提出基于树结构Parzen估计器优化集成学习的短期负荷预测方法,可对方法结构与参数进行快速寻优.将该方法应用于中国南方某省短期负荷预测,以实际算例验证了其对预测精度的有效提升.

关键词： 短期负荷预测; 树结构Parzen估计器; 集成学习; 超参优化

本文引用格式

罗敏 , 杨劲锋 , 俞蕙 , 赖雨辰 , 郭杨运 , 周尚礼 , 向睿 , 童星 , 陈潇 . 基于树结构Parzen估计器优化集成学习的短期负荷预测方法[J]. 上海交通大学学报, 2024 , 58(6) : 819 -825 . DOI: 10.16183/j.cnki.jsjtu.2022.483

Abstract

Short-term load forecasting is generally applied in power system real-time dispatching and day-ahead generation planning, which is of great significance for power system economic dispatching and safe operation of the system. Many researches on short-term load forecasting using smart models have been conducted at home and abroad. However, how to obtain the optimal structure and parameters accurately and quickly poses a challenge to short-term load forecasting, because the prediction performance of smart forecasting methods is more easily affected by the structure and parameters of the method, and the personality difference of the prediction object itself makes it difficult for the parameters to be reused. Aiming at this problem, a tree-structured Parzen estimator (TPE)-based boosting short-term load forecasting method is proposed. The results show that the proposed method can achieve rapid optimization of structure and parameters, which is verified in the application in short-term load forecasting of a southern province in China to improve the prediction accuracy.

Key words： short-term load forecasting; tree-structured Parzen estimator (TPE); ensemble learning; hyperparameter optimization

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