基于数字孪生与多模型融合的多元负荷短期预测

冯佳威; 王海鑫; 杨子豪; 陈哲; 李云路; 杨俊友

doi:10.19912/j.0254-0096.tynxb.2023-0864

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (10) : 97-106. DOI: 10.19912/j.0254-0096.tynxb.2023-0864

基于数字孪生与多模型融合的多元负荷短期预测

冯佳威¹, 王海鑫¹, 杨子豪¹, 陈哲², 李云路¹, 杨俊友¹

作者信息 +

SHORT-TERM FORECASTING OF MULTIVARIATE LOAD BASED ON DIGITAL TWIN AND MULTI-MODEL FUSION

Feng Jiawei¹, Wang Haixin¹, Yang Zihao¹, Chen Zhe², Li Yunlu¹, Yang Junyou¹

Author information +

文章历史 +

摘要

针对多元负荷呈波动性和非线性特性导致预测模型稳定性差和精确度低等问题,提出一种基于数字孪生与多模型融合的多元负荷短期预测方法。首先,根据数字孪生体中气象和负荷信息,利用最大信息系数（MIC）分析多源数据信息间的耦合特性,基于数据时序性和周期性构建筛选信息特征。其次,采用自适应局部迭代滤波（ALIF）将历史多元负荷数据进行分解,得到不同频率下固有模态函数（IMF）分量。然后,采用核极限学习机（KELM）和双向长短期记忆网络（BiLSTM）预测高频和低频负荷分量,融合重构得到初始负荷短期预测结果。最后,利用数字孪生体补偿初始预测结果,得到最终负荷预测结果。仿真结果表明,与单预测模型及未基于数字孪生预测模型相比,所提方法具有更好的稳定性,能有效应对负荷波动变化和非线性,提升模型预测精度。

Abstract

Aiming at the problems of poor stability and low accuracy of the forecasting model caused by the volatility and nonlinear of multivariate load, a short-term forecasting method of multivariate load based on digital twin and multi-model fusion is proposed. Firstly, according to the meteorological and load information in the digital twin, the maximum information coefficient （MIC） is used to analyze the coupling characteristics between multi-source data information. Information features are constructed and filtered based on data temporality and periodicity. Secondly, adaptive local iterative filtering （ALIF） is used to decompose the historical multivariate load data to obtain intrinsic mode function （IMF） at different frequencies. Then, kernel extreme learning machine （KELM） and bi-directional long short-term memory network （BiLSTM） are used to forecast high-frequency and low-frequency load components. The short-term forecasting results of the initial load are obtained by fusing and reconstructing high-frequency and low-frequency components. Finally, the final load forecasting results are obtained by compensating the initial forecasting results with the digital twin. Compared with the single forecasting model and the non-digital twin forecasting model, the proposed method can effectively deal with the fluctuation and nonlinearity of multivariate load, and has better stability and accuracy.

导出引用

冯佳威, 王海鑫, 杨子豪, 陈哲, 李云路, 杨俊友. 基于数字孪生与多模型融合的多元负荷短期预测[J]. 太阳能学报. 2024, 45(10): 97-106 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0864

Feng Jiawei, Wang Haixin, Yang Zihao, Chen Zhe, Li Yunlu, Yang Junyou. SHORT-TERM FORECASTING OF MULTIVARIATE LOAD BASED ON DIGITAL TWIN AND MULTI-MODEL FUSION[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 97-106 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0864

中图分类号： TM721

参考文献

[1] 黄际元, 杨俊, 黄治国, 等. 基于扩展短期预测和动态优化的储能调峰策略[J]. 太阳能学报, 2021, 42(9): 470-476.
HUANG J Y, YANG J, HUANG Z G, et al.Control strategy of energy storage power station participating in power grid peak shaving based on extended short-term load forecasting and dynamic optimization[J]. Acta energiae solaris sinica, 2021, 42(9): 470-476.
[2] FENG J W, WANG H X, YANG Z H, et al.Economic dispatch of industrial park considering uncertainty of renewable energy based on a deep reinforcement learning approach[J]. Sustainable energy, grids and networks, 2023, 34: 101050.
[3] 陈先飞, 何山, 王杰, 等. 基于MGM(1, n, r)的主动配电网中长期负荷预测[J]. 太阳能学报, 2020, 41(2): 188-193.
CHEN X F, HE S, WANG J, et al.Mid-long term load forecasting of active distribution network based on MGM(1, n, r)[J]. Acta energiae solaris sinica, 2020, 41(2): 188-193.
[4] WANG C, WANG Y, DING Z T, et al.A transformer-based method of multienergy load forecasting in integrated energy system[J]. IEEE transactions on smart grid, 2022, 13(4): 2703-2714.
[5] KIM N, PARK H, LEE J, et al.Short-term electrical load forecasting with multidimensional feature extraction[J]. IEEE transactions on smart grid, 2022, 13(4): 2999-3013.
[6] 杨帆, 申亚, 李东东, 等. 基于GA-GNNM的极地光伏发电功率预测方法[J]. 太阳能学报, 2022, 43(4): 167-174.
YANG F, SHEN Y, LI D D, et al.Polar photovoltaic power forecasting method based on GA-GNNM[J]. Acta energiae solaris sinica, 2022, 43(4): 167-174.
[7] 赵峰, 孙波, 张承慧. 基于多变量相空间重构和卡尔曼滤波的冷热电联供系统负荷预测方法[J]. 中国电机工程学报, 2016, 36(2): 399-406.
ZHAO F, SUN B, ZHANG C H.Cooling, heating and electrical load forecasting method for CCHP system based on multivariate phase space reconstruction and Kalman filter[J]. Proceedings of the CSEE, 2016, 36(2): 399-406.
[8] 罗丹, 温和, 唐璐. 电网动态频率测量的移频迭代滤波方法及应用研究[J]. 电力自动化设备, 2019, 39(5): 151-156.
LUO D, WEN H, TANG L.Research on frequency-shifting iterative filtering method for dynamic frequency measurement of power system and its application[J]. Electric power automation equipment, 2019, 39(5): 151-156.
[9] LYU L L, WU Z Y, ZHANG J H, et al.A VMD and LSTM based hybrid model of load forecasting for power grid security[J]. IEEE transactions on industrial informatics, 2022, 18(9): 6474-6482.
[10] LI J, DENG D Y, ZHAO J B, et al.A novel hybrid short-term load forecasting method of smart grid using MLR and LSTM neural network[J]. IEEE transactions on industrial informatics, 2021, 17(4): 2443-2452.
[11] 曾林俊, 许加柱, 王家禹, 等. 考虑区间构造的改进极限学习机短期电力负荷区间预测[J]. 电网技术, 2022, 46(7): 2555-2563.
ZENG L J, XU J Z, WANG J Y, et al.Short-term electrical load interval forecasting based on improved extreme learning machine considering interval construction[J]. Power system technology, 2022, 46(7): 2555-2563.
[12] 赵洋, 王瀚墨, 康丽, 等. 基于时间卷积网络的短期电力负荷预测[J]. 电工技术学报, 2022, 37(5): 1242-1251.
ZHAO Y, WANG H M, KANG L, et al.Temporal convolution network-based short-term electrical load forecasting[J]. Transactions of China Electrotechnical Society, 2022, 37(5): 1242-1251.
[13] 毕贵红, 赵鑫, 李璐, 等. 双模式分解CNN-LSTM集成的短期风速预测模型[J]. 太阳能学报, 2023, 44(3): 191-197.
BI G H, ZHAO X, LI L, et al.Dual-mode decomposition CNN-LSTM integrated short-term wind speed forecasting model[J]. Acta energiae solaris sinica, 2023, 44(3): 191-197.
[14] 朱凌建, 荀子涵, 王裕鑫, 等. 基于CNN-BiLSTM的短期电力负荷预测[J]. 电网技术, 2021, 45(11): 4532-4539.
ZHU L J, XUN Z H, WANG Y X, et al.Short-term power load forecasting based on CNN-BiLSTM[J]. Power system technology, 2021, 45(11): 4532-4539.
[15] 张淑清, 李君, 姜安琦, 等. 基于FPA-VMD和BiLSTM神经网络的新型两阶段短期电力负荷预测[J]. 电网技术, 2022, 46(8): 3269-3279.
ZHANG S Q, LI J, JIANG A Q, et al.A novel two-stage model based on FPA-VMD and BiLSTM neural network for short-term power load forecasting[J]. Power system technology, 2022, 46(8): 3269-3279.
[16] HOSAMO H H, NIELSEN H K, KRANIOTIS D, et al.Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings[J]. Energy and buildings, 2023, 281: 112732.
[17] 史凯钰, 张东霞, 韩肖清, 等. 基于LSTM与迁移学习的光伏发电功率预测数字孪生模型[J]. 电网技术, 2022, 46(4): 1363-1372.
SHI K Y, ZHANG D X, HAN X Q, et al.Digital twin model of photovoltaic power generation prediction based on LSTM and transfer learning[J]. Power system technology, 2022, 46(4): 1363-1372.
[18] 孙荣富, 王隆扬, 王玉林, 等. 基于数字孪生的光伏发电功率超短期预测[J]. 电网技术, 2021, 45(4): 1258-1264.
SUN R F, WANG L Y, WANG Y L, et al.Ultra-short-term prediction of photovoltaic power generation based on digital twins[J]. Power system technology, 2021, 45(4): 1258-1264.
[19] 赵鹏, 蒲天骄, 王新迎, 等. 面向能源互联网数字孪生的电力物联网关键技术及展望[J]. 中国电机工程学报, 2022, 42(2): 447-458.
ZHAO P, PU T J, WANG X Y, et al.Key technologies and perspectives of power Internet of Things facing with digital twins of the energy Internet[J]. Proceedings of the CSEE, 2022, 42(2): 447-458.
[20] 欧阳静, 杨吕, 尹康, 等. 基于ALIF-LSTM多任务学习的综合能源系统短期负荷预测[J]. 太阳能学报, 2022, 43(9): 499-507.
OUYANG J, YANG L, YIN K, et al.Short-term load forecasting method for integrated energy system based on ALIF-LSTM and multi-task learning[J]. Acta energiae solaris sinica, 2022, 43(9): 499-507.
[21] 王琛, 王颖, 郑涛, 等. 基于ResNet-LSTM网络和注意力机制的综合能源系统多元负荷预测[J]. 电工技术学报, 2022, 37(7): 1789-1799.
WANG C, WANG Y, ZHENG T, et al.Multi-energy load forecasting in integrated energy system based on ResNet-LSTM network and attention mechanism[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1789-1799.
[22] 黄文聪, 潘风, 杨子潇, 等. 基于改进核极限学习机的风电功率短期预测[J]. 重庆理工大学学报(自然科学), 2023, 37(2): 241-250.
HUANG W C, PAN F, YANG Z X, et al.Short-term prediction of wind power based on improved kernel extreme learning machines[J]. Journal of Chongqing University of Technology (natural science), 2023, 37(2): 241-250.
[23] GUO Y X, LI Y, QIAO X B, et al.BiLSTM multitask learning-based combined load forecasting considering the loads coupling relationship for multienergy system[J]. IEEE transactions on smart grid, 2022, 13(5): 3481-3492.