基于ICEEMDAN-PE和IDBO-Informer组合模型的短期负荷预测

doi:10.11930/j.issn.1004-9649.202410086

中国电力 ›› 2025, Vol. 58 ›› Issue (6): 19-32.DOI: 10.11930/j.issn.1004-9649.202410086

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基于ICEEMDAN-PE和IDBO-Informer组合模型的短期负荷预测

于多¹(), 曹燚²(), 王海荣¹(), 赵翱东¹, 曹倩³

1. 无锡职业技术学院控制工程学院，江苏无锡　214000
2. 无锡学院物联网工程学院，江苏无锡　214105
3. 南京信息工程大学自动化学院，江苏南京　210000

收稿日期:2024-10-28 发布日期:2025-06-30 出版日期:2025-06-28
作者简介:
于多（1984），女，通信作者，高级工程师，从事智能终端与物联网应用以及用电负荷预测研究，E-mail：yduo20211249605@126.com
曹燚（1994），男，讲师，从事计算机视觉、车联网安全等研究，E-mail：caoyi@cwxu.edu.cn（第二十七届中国科协年会学术论文“配微储协同的低碳高品质新型配电系统”专题）
基金资助:
国家自然科学基金资助项目（42305158）；第二批国家级职业教育教师教学创新团队课题研究项目（ZI2021030103）；江苏省高等教育教改研究立项重点课题（2021JSJG197）。

Short-term Load Forecasting Based on a Combined ICEEMDAN-PE and IDBO-Informer Model

YU Duo¹(), CAO Yi²(), WANG Hairong¹(), ZHAO Aodong¹, CAO Qian³

1. School of Control Engineering, Wuxi Institute of Technology, Wuxi 214000, China
2. School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China
3. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210000, China

Received:2024-10-28 Online:2025-06-30 Published:2025-06-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.42305158); The Second Batch of National Vocational Education Teachers' Teaching Innovation Team Research Project (No.ZI2021030103); Key Project of Jiangsu Higher Education Reform Research Project (No.2021JSJG197).

摘要/Abstract

摘要：

针对传统方法在处理复杂负荷数据时存在的噪声处理不足、特征提取能力有限及模型训练复杂等问题，提出了一种基于改进完全集合经验模态分解（improved complete ensemble empirical mode decomposition with adaptive noise，ICEEMDAN）-置换熵（permutation entropy，PE）和改进蜣螂优化算法（improved dung beetle optimizer，IDBO）-Informer的创新组合预测模型。首先，该模型通过小波软阈值去噪算法预处理原始负荷数据，减少噪声干扰。其次，利用ICEEMDAN多尺度分解负荷数据，精准捕捉负荷特征，并采用置换熵评估分量复杂度。最后，对蜣螂优化算法进行改进，通过融合混沌与逆向学习策略进行种群初始化，引入自适应步长与凸透镜逆成像策略及随机差异变异策略，优化Informer预测模型参数，显著提升预测效率与准确性。实验结果表明，该模型在短期负荷预测中表现出色，平均绝对误差为81.3 MW（原始负荷数据范围约为500 MW至1 500 MW），均方根误差为109.2 MW，拟合系数评分为0.991，远优于传统方法，充分验证了模型的创新性和优越性。

关键词: 负荷预测, ICEEMDAN, 改进蜣螂优化算法, Informer

Abstract:

To address the problems of insufficient noise processing, limited feature extraction ability and complex model training when using traditional methods to deal with complex load data, an innovative forecasting model based on a combined ICEEMDAN-PE and IDBO-Informer is proposed. Firstly, the raw load data were preprocessed using wavelet soft-threshold denoising algorithm to reduce noise interference. Secondly, ICEEMDAN was used for multi-scale decomposition of load data to precisely characterize load features, and the permutation entropy was used to evaluate the component complexity. Finally, an improved Dung Beetle Optimizer (IDBO) was proposed by synergistically integrating chaotic and opposition-based learning strategies for population initialization, incorporating adaptive step size, convex lens opposition imaging, and stochastic differential mutation strategies. This approach optimizes hyperparameters of the Informer forecasting model, significantly enhancing computational efficiency and prediction accuracy. The experimental results show that the model performs well in short-term load forecasting, with MAE of 81.3 MW (the original load data range is about 500 MW to 1500 MW), RMSE of 109.2 MW and R² score of 0.991, which is much better than the traditional method, and fully verifies the innovation and superiority of the model.

Key words: load forecasting, ICEEMDAN, improved dung beetle optimizer algorithm, Informer

于多, 曹燚, 王海荣, 赵翱东, 曹倩. 基于ICEEMDAN-PE和IDBO-Informer组合模型的短期负荷预测[J]. 中国电力, 2025, 58(6): 19-32.

YU Duo, CAO Yi, WANG Hairong, ZHAO Aodong, CAO Qian. Short-term Load Forecasting Based on a Combined ICEEMDAN-PE and IDBO-Informer Model[J]. Electric Power, 2025, 58(6): 19-32.

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图/表 19

参考文献 26

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算法		参数设置
PSO		C₁=C₂=2，w₁=0.8
SSA		D=0.3，T=0.1，R₂=0.6
GWO		f_i=2，A=0.1，w₂=0.5
DBO		K₁=0.1，S₁=0.5
IDBO		K₂=0.1，S₂=0.5

算法		参数设置
PSO		C₁=C₂=2，w₁=0.8
SSA		D=0.3，T=0.1，R₂=0.6
GWO		f_i=2，A=0.1，w₂=0.5
DBO		K₁=0.1，S₁=0.5
IDBO		K₂=0.1，S₂=0.5

测试函数	算法	BV	WV	MV	SD	测试函数	算法	BV	WV	MV	SD
f₁	PSO	5.6797×10^–90	5.3574×10^–87	6.6176×10^–88	1.2604×10^–87	f₄	PSO	1.3712×10^–91	8.4084×10^–88	1.3777×10^–88	8.4084×10^–88
	SSA	0	1.8782×10^–57	6.2805×10^–59	3.4288×10^–58		SSA	2.2061×10^–56	0	8.2669×10^–58	4.0257×10^–57
	GWO	1.7348×10^–29	5.9903×10^–27	1.0541×10^–27	1.3803×10^–27		GWO	9.3765×10^–30	1.9702×10^–27	1.6280×10^–28	3.6480×10^–28
	DBO	5.0220×10^–152	7.1639×10^–112	2.5442×10^–113	1.3078×10^–112		DBO	4.1327×10^–166	1.4584×10^–104	5.0713×10^–105	2.6613×10^–105
	IDBO	0	0	0	0		IDBO	0	0	0	0

测试函数	算法	BV	WV	MV	SD	测试函数	算法	BV	WV	MV	SD
f₅	PSO	1.5772×10^–45	9.5814×10^–47	8.0128×10^–45	1.1912×10^–45	f₁₁	PSO	9.5097×10^–183	8.4709×10^–174	8.6362×10^–175	0
	SSA	1.0629×10^–107	1.3856×10^–28	4.6849×10^–30	2.5287×10^–29		SSA	6.2132×10^–202	1.7317×10^–50	5.7724×10^–52	3.1616×10^–51
	GWO	2.3430×10^–17	2.4452×10^–16	1.0058×10^–16	6.3196×10^–17		GWO	2.6723×10^–17	5.4980×10^–85	1.8424×10^–86	1.0036×10^–85
	DBO	1.4972×10^–83	3.6254×10^–54	1.2086×10^–55	6.6191×10^–55		DBO	1.0159×10^–187	4.6142×10^–112	1.5430×10^–113	8.4235×10^–113
	IDBO	0	0	0	0		IDBO	0	0	0	0

测试函数	算法	BV	WV	MV	SD	测试函数	算法	BV	WV	MV	SD
f₁₅	PSO	0	0	0	0	f₂₀	PSO	4.4409×10^–15	7.9936×10^–15	6.0988×10^–15	1.8027×10^–15
	SSA	0	0	0	0		SSA	8.8818×10^–16	8.8818×10^–16	8.8818×10^–16	0
	GWO	5.6843×10^–14	8.2608	1.7812	2.5556		GWO	6.4838×10^–14	1.4655×10^–13	0	0
	DBO	0	9.1724	0.30575	1.6746		DBO	8.8818×10^–16	8.8818×10^–16	8.8818×10^–16	0
	IDBO	8.8818×10^–16	8.8818×10^–16	8.8818×10^–16	0		IDBO	0	0	0	0

测试函数	算法	BV	WV	MV	SD	测试函数	算法	BV	WV	MV	SD
f₁	PSO	5.6797×10^–90	5.3574×10^–87	6.6176×10^–88	1.2604×10^–87	f₄	PSO	1.3712×10^–91	8.4084×10^–88	1.3777×10^–88	8.4084×10^–88
	SSA	0	1.8782×10^–57	6.2805×10^–59	3.4288×10^–58		SSA	2.2061×10^–56	0	8.2669×10^–58	4.0257×10^–57
	GWO	1.7348×10^–29	5.9903×10^–27	1.0541×10^–27	1.3803×10^–27		GWO	9.3765×10^–30	1.9702×10^–27	1.6280×10^–28	3.6480×10^–28
	DBO	5.0220×10^–152	7.1639×10^–112	2.5442×10^–113	1.3078×10^–112		DBO	4.1327×10^–166	1.4584×10^–104	5.0713×10^–105	2.6613×10^–105
	IDBO	0	0	0	0		IDBO	0	0	0	0

测试函数	算法	BV	WV	MV	SD	测试函数	算法	BV	WV	MV	SD
f₅	PSO	1.5772×10^–45	9.5814×10^–47	8.0128×10^–45	1.1912×10^–45	f₁₁	PSO	9.5097×10^–183	8.4709×10^–174	8.6362×10^–175	0
	SSA	1.0629×10^–107	1.3856×10^–28	4.6849×10^–30	2.5287×10^–29		SSA	6.2132×10^–202	1.7317×10^–50	5.7724×10^–52	3.1616×10^–51
	GWO	2.3430×10^–17	2.4452×10^–16	1.0058×10^–16	6.3196×10^–17		GWO	2.6723×10^–17	5.4980×10^–85	1.8424×10^–86	1.0036×10^–85
	DBO	1.4972×10^–83	3.6254×10^–54	1.2086×10^–55	6.6191×10^–55		DBO	1.0159×10^–187	4.6142×10^–112	1.5430×10^–113	8.4235×10^–113
	IDBO	0	0	0	0		IDBO	0	0	0	0

测试函数	算法	BV	WV	MV	SD	测试函数	算法	BV	WV	MV	SD
f₁₅	PSO	0	0	0	0	f₂₀	PSO	4.4409×10^–15	7.9936×10^–15	6.0988×10^–15	1.8027×10^–15
	SSA	0	0	0	0		SSA	8.8818×10^–16	8.8818×10^–16	8.8818×10^–16	0
	GWO	5.6843×10^–14	8.2608	1.7812	2.5556		GWO	6.4838×10^–14	1.4655×10^–13	0	0
	DBO	0	9.1724	0.30575	1.6746		DBO	8.8818×10^–16	8.8818×10^–16	8.8818×10^–16	0
	IDBO	8.8818×10^–16	8.8818×10^–16	8.8818×10^–16	0		IDBO	0	0	0	0

数据集	模型	MAE/ MW	RMSE/ MW	R²
一区	ICEEMDAN-PE-IDBO-SVR	265.7	296.8	0.947
	ICEEMDAN-PE-IDBO-LSTM	190.5	226.3	0.965
	ICEEMDAN-PE-IDBO-GRU	140.8	169.1	0.972
	ICEEMDAN-PE-IDBO-Transformer	90.8	121.4	0.980
	ICEEMDAN-PE-IDBO-Informer	81.3	109.2	0.991
二区	ICEEMDAN-PE-IDBO-SVR	1397.6	1685.1	0.941
	ICEEMDAN-PE-IDBO-LSTM	694.7	877.2	0.962
	ICEEMDAN-PE-IDBO-GRU	557.6	706.6	0.979
	ICEEMDAN-PE-IDBO-Transformer	493.3	585.8	0.985
	ICEEMDAN-PE-IDBO-Informer	331.8	384.1	0.995

基于ICEEMDAN-PE和IDBO-Informer组合模型的短期负荷预测

Short-term Load Forecasting Based on a Combined ICEEMDAN-PE and IDBO-Informer Model

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参考文献 26

相关文章 15

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Metrics

数据集	模型	MAE/MW	RMSE/MW	R²
一区	no-WSTD	267.6	298.2	0.944
	no-ICEEMDAN	301.3	387.7	0.916
	no-PE	286.4	353.1	0.939
	no-IDBO	346.8	452.4	0.901
	ICEEMDAN-PE-IDBO-Informer	81.3	109.2	0.991
二区	no-WSTD	1215.4	1485.1	0.946
	no-ICEEMDAN	1551.6	1816.7	0.918
	no-PE	1430.3	1776.2	0.937
	no-IDBO	1733.5	1938.6	0.908
	ICEEMDAN-PE-IDBO-Informer	331.8	384.1	0.995

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[3]	王宇飞, 杜桐, 边伟国, 张钊, 刘慧婷, 杨丽君. 基于DTW K-medoids与VMD-多分支神经网络的多用户短期负荷预测[J]. 中国电力, 2024, 57(6): 121-130.
[4]	吴军英, 路欣, 刘宏, 张彬, 柴守亮, 刘蕴春, 王佳楠. 基于Spearman-GCN-GRU模型的超短期多区域电力负荷预测[J]. 中国电力, 2024, 57(6): 131-140.
[5]	陈中飞, 赵越, 蔡秋娜, 张乔榆, 王泽林, 戴晓娟, 陈雨果. 基于净负荷预测误差统计的电力系统爬坡能力充裕度评估[J]. 中国电力, 2024, 57(5): 50-60.
[6]	刘航, 申皓, 杨勇, 纪陵, 余洋. 基于高阶马尔可夫链的纯电重卡集群负荷预测[J]. 中国电力, 2024, 57(5): 61-69.
[7]	李丹, 贺帅, 颜伟, 胡越, 方泽仁, 梁云嫣. 考虑动态时间锚点和典型特征约束的年日均负荷曲线预测[J]. 中国电力, 2024, 57(11): 36-47.
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[9]	唐旭辰, 潮铸, 段秦尉, 苏炳洪, 陈卉灿. 基于分层测量数据的高压变电站概率负荷预测方法[J]. 中国电力, 2023, 56(8): 143-150.
[10]	贾巍, 黄裕春. 基于小样本数据差分扩容的微电网负荷预测方法[J]. 中国电力, 2023, 56(8): 151-156,165.
[11]	彭生江, 杨德州, 孙传帅, 袁铁江, 刘永成. 基于氢负荷需求的氢能系统容量规划[J]. 中国电力, 2023, 56(7): 13-20,32.
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数据集	模型	MAE/ MW	RMSE/ MW	R²
一区	ICEEMDAN-PE-PSO-Informer	138.7	169.9	0.974
	ICEEMDAN-PE-GSA-Informer	136.1	167.8	0.975
	ICEEMDAN-PE-GWO-Informer	133.2	161.4	0.979
	ICEEMDAN-PE-BOA-Informer	89.2	119.8	0.983
	ICEEMDAN-PE-DBO-Informer	87.8	117.9	0.985
	ICEEMDAN-PE-IDBO-Informer	81.3	109.2	0.991
二区	ICEEMDAN-PE-PSO-Informer	568.7	726.3	0.977
	ICEEMDAN-PE-GSA-Informer	511.2	603.4	0.981
	ICEEMDAN-PE-GWO-Informer	492.4	585.2	0.984
	ICEEMDAN-PE-BOA-Informer	391.2	563.5	0.989
	ICEEMDAN-PE-DBO-Informer	373.9	418.8	0.992
	ICEEMDAN-PE-IDBO-Informer	331.8	384.1	0.995

数据集	模型	MAE/ MW	RMSE/ MW	R²
一区	ICEEMDAN-PE-PSO-Informer	138.7	169.9	0.974
	ICEEMDAN-PE-GSA-Informer	136.1	167.8	0.975
	ICEEMDAN-PE-GWO-Informer	133.2	161.4	0.979
	ICEEMDAN-PE-BOA-Informer	89.2	119.8	0.983
	ICEEMDAN-PE-DBO-Informer	87.8	117.9	0.985
	ICEEMDAN-PE-IDBO-Informer	81.3	109.2	0.991
二区	ICEEMDAN-PE-PSO-Informer	568.7	726.3	0.977
	ICEEMDAN-PE-GSA-Informer	511.2	603.4	0.981
	ICEEMDAN-PE-GWO-Informer	492.4	585.2	0.984
	ICEEMDAN-PE-BOA-Informer	391.2	563.5	0.989
	ICEEMDAN-PE-DBO-Informer	373.9	418.8	0.992
	ICEEMDAN-PE-IDBO-Informer	331.8	384.1	0.995

数据集	模型	MAE/ MW	RMSE/ MW	R²
一区	EMD-PE-IDBO-Informer	272.6	310.7	0.934
	EEMD-PE-IDBO-Informer	236.3	279.2	0.956
	CEEMD-PE-IDBO-Informer	187.3	219.6	0.969
	CEEMDAN-PE-IDBO-Informer	146.5	186.8	0.971
	ICEEMDAN-PE-IDBO-Informer	81.3	109.2	0.991
二区	EMD-PE-IDBO-Informer	1425.4	1763.2	0.933
	EEMD-PE-IDBO-Informer	1161.2	1396.7	0.948
	CEEMD-PE-IDBO-Informer	646.7	796.2	0.967
	CEEMDAN-PE-IDBO-Informer	541.2	715.4	0.978
	ICEEMDAN-PE-IDBO-Informer	331.8	384.1	0.995

数据集	模型	MAE/ MW	RMSE/ MW	R²
一区	EMD-PE-IDBO-Informer	272.6	310.7	0.934
	EEMD-PE-IDBO-Informer	236.3	279.2	0.956
	CEEMD-PE-IDBO-Informer	187.3	219.6	0.969
	CEEMDAN-PE-IDBO-Informer	146.5	186.8	0.971
	ICEEMDAN-PE-IDBO-Informer	81.3	109.2	0.991
二区	EMD-PE-IDBO-Informer	1425.4	1763.2	0.933
	EEMD-PE-IDBO-Informer	1161.2	1396.7	0.948
	CEEMD-PE-IDBO-Informer	646.7	796.2	0.967
	CEEMDAN-PE-IDBO-Informer	541.2	715.4	0.978
	ICEEMDAN-PE-IDBO-Informer	331.8	384.1	0.995

模态框（Modal）标题

基于ICEEMDAN-PE和IDBO-Informer组合模型的短期负荷预测

Short-term Load Forecasting Based on a Combined ICEEMDAN-PE and IDBO-Informer Model

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