基于树结构Parzen估计器优化集成学习的短期负荷预测方法

doi:10.16183/j.cnki.jsjtu.2022.483

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (6): 819-825.doi: 10.16183/j.cnki.jsjtu.2022.483

• 新型电力系统与综合能源 • 上一篇下一篇

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¹, 杨劲锋², 俞蕙², 赖雨辰¹, 郭杨运¹, 周尚礼¹, 向睿¹, 童星³(), 陈潇³

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

收稿日期:2022-11-28 修回日期:2023-02-17 接受日期:2023-03-09 出版日期:2024-06-28 发布日期:2024-07-05
通讯作者: 童星,博士,电话(Tel.):0755-84860840;E-mail: tongxing@orait.cn.
作者简介: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.
基金资助:
南方电网数字电网研究研究院(670000KK52210036)

TPE-Based Boosting Short-Term Load Forecasting Method

LUO Min¹, YANG Jinfeng², YU Hui², LAI Yuchen¹, GUO Yangyun¹, ZHOU Shangli¹, XIANG Rui¹, TONG Xing³(), CHEN Xiao³

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:2022-11-28 Revised:2023-02-17 Accepted:2023-03-09 Online:2024-06-28 Published:2024-07-05

摘要/Abstract

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

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

中图分类号:

TM712

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.

LUO Min, YANG Jinfeng, YU Hui, LAI Yuchen, GUO Yangyun, ZHOU Shangli, XIANG Rui, TONG Xing, CHEN Xiao. TPE-Based Boosting Short-Term Load Forecasting Method[J]. Journal of Shanghai Jiao Tong University, 2024, 58(6): 819-825.

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参考文献 20

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超参数	寻优空间
输入历史负荷数据	离散变量,取为集合{第(n-1)d的j时段实际负荷,第(n-1)、(n-7)d的j段实际负荷,第(n-1)、(n-7)、(n-14)d的j时段实际负荷}中的一种.
输入相关因素数据	确定值,取为第n d的j时段气象因素.
输出数据	确定值,取为第n d的j时段预测负荷.
学习率	连续变量,取值空间为[0.01,1].
学习器数量	离散整数变量,取为[10,200]之间的某一整数.

参数	取值
输入数据	第(n-1) d的j时段实际负荷第n d的j时段气象因素
输出数据	第n d的j时段预测负荷
学习率	0.174
学习器数量	166

日期	AdaBoost-TPE	BP-ANN	日期		BP-ANN
5月11日	98.84	97.38	5月22日	97.81	96.53
5月12日	98.21	97.30	5月23日	97.84	96.10
5月13日	98.50	94.26	5月24日	98.33	95.42
5月14日	98.76	98.45	5月25日	98.51	93.67
5月15日	98.37	97.77	5月26日	97.65	95.75
5月16日	98.29	96.97	5月27日	97.74	97.55
5月17日	97.93	97.79	5月28日	97.25	97.08
5月18日	98.45	97.52	5月29日	97.56	97.35
5月19日	98.51	97.06	5月30日	97.73	96.37
5月20日	98.73	96.14	5月31日	98.44	95.52
5月21日	98.43	96.07	平均值	98.18	96.57

基于树结构Parzen估计器优化集成学习的短期负荷预测方法

TPE-Based Boosting Short-Term Load Forecasting Method

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