基于复合因子构造的KAN-BiLSTM电力负荷预测方法

doi:10.11930/j.issn.1004-9649.202504033

中国电力 ›› 2025, Vol. 58 ›› Issue (12): 178-189, 198.DOI: 10.11930/j.issn.1004-9649.202504033

• 新型电网 • 上一篇

基于复合因子构造的KAN-BiLSTM电力负荷预测方法

陈景文¹(), 黄羽倩¹(), 刘耀先¹(), 陈宋宋², 钱晓瑞³, 周颖², 詹祥澎³

1. 陕西科技大学电气与控制工程学院，陕西西安　710021
2. 需求侧多能互补优化与供需互动技术北京市重点实验室（中国电力科学研究院有限公司），北京　100192
3. 国网福建营销服务中心，福建福州　350013

收稿日期:2025-04-12 修回日期:2025-12-08 发布日期:2025-12-27 出版日期:2025-12-28
作者简介:
陈景文（1978），男，教授，从事电动汽车充电负荷优化调度、微电网、混合储能技术研究，E-mail：chenjw@sust.edu.cn
黄羽倩（2001），女，硕士研究生，从事电力负荷预测研究，E-mail：230611011@sust.edu.cn
刘耀先（1990），男，通信作者，博士，从事电力电量预测、虚拟电厂、非侵入式负荷监测分解研究，E-mail：lpxlyx@126.com
基金资助:
国家自然科学基金资助项目（52407059）；国家电网有限公司科技项目（5400-202321572A-3-2-ZN）。

A KAN-BiLSTM-based Power Load Forecasting Method Utilizing Composite Factor Construction

CHEN Jingwen¹(), HUANG Yuqian¹(), LIU Yaoxian¹(), CHEN Songsong², QIAN Xiaorui³, ZHOU Ying², ZHAN Xiangpeng³

1. School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
2. Beijing Key Laboratory of Demand-Side Multi-energy Optimization and Supply-Demand Interaction Technology (China Electric Power Research Institute Co., Ltd.), Beijing 100192, China
3. State Grid Fujian Marketing Service Center, Fuzhou 350013, China

Received:2025-04-12 Revised:2025-12-08 Online:2025-12-27 Published:2025-12-28
Supported by:
This work is supported by the National Natural Science Foundation of China (No.52407059); Science and Technology Project of SGCC (No.5400-202321572A-3-2-ZN).

摘要/Abstract

摘要：

针对未充分考虑气象因子交互作用、模型非线性表达能力存在局限性等问题，基于复合因子构造提出一种结合科尔莫戈洛夫-阿诺德网络（Kolmogorov- Arnold network，KAN）与双向长短期记忆（bi-directional long short-term memory，BiLSTM）网络的电力负荷预测方法。首先，通过高斯混合模型（Gaussian mixture model，GMM）将有相似特征的用电负荷曲线归类。其次，提出复合因子构造策略，通过皮尔逊相关性分析量化气象因子与负荷的线性关联度，筛选关键气象变量并构造交互项，充分挖掘气象因素间潜在交互作用，结合最大信息系数（maximal information coefficient，MIC）进一步提取非线性依赖特征。最后，针对传统BiLSTM模型全连接层对高维非线性特征学习能力受限的问题，引入KAN替代全连接层，利用其非线性映射能力，构建KAN-BiLSTM混合预测模型。基于某地区实际数据进行算例分析，实验结果表明，在春秋日、夏季常温日、夏季高温日、冬季日4类不同负荷模式下所提方法均具有较高的预测准确率和普适性，可为多气象耦合场景下的电力负荷精准预测提供一种可行的解决方案。

关键词: 负荷预测, 复合因子构造, 双向长短期记忆网络, Kolmogorov-Arnold网络, 非线性表征

Abstract:

To address such problems as the insufficient consideration of the interaction of meteorological factors, and the limitations of model's nonlinear expression ability, a power load forecasting method based on Kolmogorov-Arnold network (KAN) and bidirectional long short-term memory (BiLSTM) network utilizing composite factor construction is proposed. Firstly, the power load curves with similar characteristics are classified through the Gaussian mixture model. Secondly, a composite factor construction strategy is proposed: the linear correlation degree between meteorological factors and loads is quantified through Pearson correlation analysis; key meteorological variables are screened out and interaction terms are constructed to fully explore the potential interaction among meteorological factors, and nonlinear dependent features are further extracted with maximum information coefficient. Finally, aiming at the problem that the fully connected layer of the traditional BiLSTM model has limited ability to learn high-dimensional nonlinear features, the KAN is introduced to replace the fully connected layer, and a KAN-BILSTM hybrid prediction model is constructed using its nonlinear mapping ability. The experimental results demonstrate that the proposed method has high prediction accuracy and universality under four different load modes. It can provide a feasible solution for the precise prediction of power load in multi-meteorological coupling scenarios.

Key words: load forecasting, composite factor construction, bidirectional long short-term memory network, Kolmogorov-Arnold network, nonlinear characterization

陈景文, 黄羽倩, 刘耀先, 陈宋宋, 钱晓瑞, 周颖, 詹祥澎. 基于复合因子构造的KAN-BiLSTM电力负荷预测方法[J]. 中国电力, 2025, 58(12): 178-189, 198.

CHEN Jingwen, HUANG Yuqian, LIU Yaoxian, CHEN Songsong, QIAN Xiaorui, ZHOU Ying, ZHAN Xiangpeng. A KAN-BiLSTM-based Power Load Forecasting Method Utilizing Composite Factor Construction[J]. Electric Power, 2025, 58(12): 178-189, 198.

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图/表 11

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负荷模式	相关系数
负荷模式	温度	相对湿度	降雨量	风速	温度- 湿度	温度- 风速	湿度- 风速
1	0.26	–0.35	0.024	0.19	–0.28	0.32	–0.34
2	0.63	0.180	0.051	0.17	0.32	0.66	0.19
3	–0.41	–0.45	0.077	0.13	–0.50	–0.40	–0.45
4	0.53	–0.52	–0.022	0.22	–0.51	0.52	–0.52

负荷模式	相关系数
负荷模式	温度	相对湿度	降雨量	风速	温度- 湿度	温度- 风速	湿度- 风速
1	0.26	–0.35	0.024	0.19	–0.28	0.32	–0.34
2	0.63	0.180	0.051	0.17	0.32	0.66	0.19
3	–0.41	–0.45	0.077	0.13	–0.50	–0.40	–0.45
4	0.53	–0.52	–0.022	0.22	–0.51	0.52	–0.52

模块	参数类型	范围	设定值
BiLSTM	隐藏层神经元/个	[16,32,64,128]	32
BiLSTM	层数	[1,2,3]	2
KAN	网格点数量	[5,10,15,20]	20
	KAN-layers		2
	Spline-order	[2,3,4]	3
	scale_spline	[0.5,1.0,1.5]	1.0
	base_activation		SiLU
	Scale-noise		0.1
其他	批大小	[16,32,64,128]	64
	训练轮次		300
	学习率	[0.0001,0.001,0.01,0.1]	0.001
	Dropout	[0.1,0.2,0.3,0.4,0.5]	0.2
	窗口长度	[24,48,96]	24
	训练优化器		Adam
	损失函数		均方误差

模块	参数类型	范围	设定值
BiLSTM	隐藏层神经元/个	[16,32,64,128]	32
BiLSTM	层数	[1,2,3]	2
KAN	网格点数量	[5,10,15,20]	20
	KAN-layers		2
	Spline-order	[2,3,4]	3
	scale_spline	[0.5,1.0,1.5]	1.0
	base_activation		SiLU
	Scale-noise		0.1
其他	批大小	[16,32,64,128]	64
	训练轮次		300
	学习率	[0.0001,0.001,0.01,0.1]	0.001
	Dropout	[0.1,0.2,0.3,0.4,0.5]	0.2
	窗口长度	[24,48,96]	24
	训练优化器		Adam
	损失函数		均方误差

a）春秋日
模型	e_MAPE/%	e_MAE/kW	e_RMSE/kW	P_precision/%
CF-TCN	5.02	261.68	366.63	94.98
CF-LSTM	5.53	290.22	402.85	94.47
CF-KAN-LSTM	3.87	198.44	279.18	96.13
BiLSTM	5.25	270.85	366.41	94.75
KAN-BiLSTM	4.87	253.38	341.02	95.13
CF-BiLSTM	3.37	183.55	257.23	96.63
CF-KAN-BiLSTM	3.21	171.20	237.80	96.79
文献[36]	3.65	192.55	271.19	96.35

b）夏季常温日
模型	e_MAPE/%	e_MAE/kW	e_RMSE/kW	P_precision/%
CF-TCN	6.38	494.45	641.57	93.62
CF-LSTM	5.93	479.29	599.51	94.07
CF-KAN-LSTM	5.52	438.78	572.38	94.48
BiLSTM	5.83	452.10	574.67	94.17
KAN-BiLSTM	5.53	443.52	573.29	94.47
CF-BiLSTM	5.67	455.87	597.12	94.33
CF-KAN-BiLSTM	4.62	365.68	469.58	95.38
文献[36]	5.45	435.74	548.26	94.55

c）夏季高温日
模型	e_MAPE/%	e_MAE/kW	e_RMSE/kW	P_precision/%
CF-TCN	7.29	426.27	546.55	92.71
CF-LSTM	6.97	412.92	532.50	93.03
CF-KAN-LSTM	6.70	397.83	507.89	93.30
BiLSTM	6.62	374.62	540.08	93.38
KAN-BiLSTM	5.96	342.08	514.44	94.04
CF-BiLSTM	6.02	343.21	500.46	93.98
CF-KAN-BiLSTM	5.77	329.78	490.80	94.23
文献[36]	6.10	345.80	533.41	93.90

d）冬季日
模型	e_MAPE/%	e_MAE/kW	e_RMSE/kW	P_precision/%
CF-TCN	7.13	748.46	963.27	92.87
CF-LSTM	7.91	846.05	1082.77	92.09
CF-KAN-LSTM	6.93	730.34	927.14	93.07
BiLSTM	6.42	686.20	830.40	93.58
KAN-BiLSTM	5.86	624.47	751.50	94.14
CF-BiLSTM	6.17	654.48	800.34	93.83
CF-KAN-BiLSTM	5.77	618.99	733.86	94.23
文献[36]	6.99	728.4	833.53	93.01

基于复合因子构造的KAN-BiLSTM电力负荷预测方法

A KAN-BiLSTM-based Power Load Forecasting Method Utilizing Composite Factor Construction

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参考文献 36

相关文章 15

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分类	训练时间/s
分类	CF-BiLSTM	CF-KAN-BiLSTM
春秋日	246	224
夏季常温日	76	67
夏季高温日	107	73
冬季日	30	27

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模态框（Modal）标题

基于复合因子构造的KAN-BiLSTM电力负荷预测方法

A KAN-BiLSTM-based Power Load Forecasting Method Utilizing Composite Factor Construction

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 36

相关文章 15

编辑推荐

Metrics